Instructions to use alexgusevski/granite-3.2-2b-instruct-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexgusevski/granite-3.2-2b-instruct-mlx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="alexgusevski/granite-3.2-2b-instruct-mlx") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("alexgusevski/granite-3.2-2b-instruct-mlx") model = AutoModelForMultimodalLM.from_pretrained("alexgusevski/granite-3.2-2b-instruct-mlx") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - MLX
How to use alexgusevski/granite-3.2-2b-instruct-mlx with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("alexgusevski/granite-3.2-2b-instruct-mlx") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use alexgusevski/granite-3.2-2b-instruct-mlx with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "alexgusevski/granite-3.2-2b-instruct-mlx" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alexgusevski/granite-3.2-2b-instruct-mlx", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/alexgusevski/granite-3.2-2b-instruct-mlx
- SGLang
How to use alexgusevski/granite-3.2-2b-instruct-mlx with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "alexgusevski/granite-3.2-2b-instruct-mlx" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alexgusevski/granite-3.2-2b-instruct-mlx", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "alexgusevski/granite-3.2-2b-instruct-mlx" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alexgusevski/granite-3.2-2b-instruct-mlx", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Pi
How to use alexgusevski/granite-3.2-2b-instruct-mlx with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "alexgusevski/granite-3.2-2b-instruct-mlx"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "alexgusevski/granite-3.2-2b-instruct-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use alexgusevski/granite-3.2-2b-instruct-mlx with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "alexgusevski/granite-3.2-2b-instruct-mlx"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default alexgusevski/granite-3.2-2b-instruct-mlx
Run Hermes
hermes
- MLX LM
How to use alexgusevski/granite-3.2-2b-instruct-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "alexgusevski/granite-3.2-2b-instruct-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "alexgusevski/granite-3.2-2b-instruct-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alexgusevski/granite-3.2-2b-instruct-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use alexgusevski/granite-3.2-2b-instruct-mlx with Docker Model Runner:
docker model run hf.co/alexgusevski/granite-3.2-2b-instruct-mlx
Add files using upload-large-folder tool
Browse files- README.md +39 -0
- added_tokens.json +5 -0
- config.json +32 -0
- merges.txt +0 -0
- model.safetensors +3 -0
- model.safetensors.index.json +369 -0
- special_tokens_map.json +35 -0
- tokenizer.json +0 -0
- tokenizer_config.json +199 -0
- vocab.json +0 -0
README.md
ADDED
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---
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| 2 |
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pipeline_tag: text-generation
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inference: false
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| 4 |
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license: apache-2.0
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library_name: transformers
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| 6 |
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tags:
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| 7 |
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- language
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| 8 |
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- granite-3.2
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| 9 |
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- mlx
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| 10 |
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base_model: ibm-granite/granite-3.2-2b-instruct
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| 11 |
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---
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| 12 |
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| 13 |
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# alexgusevski/granite-3.2-2b-instruct-mlx
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The Model [alexgusevski/granite-3.2-2b-instruct-mlx](https://huggingface.co/alexgusevski/granite-3.2-2b-instruct-mlx) was
|
| 16 |
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converted to MLX format from [ibm-granite/granite-3.2-2b-instruct](https://huggingface.co/ibm-granite/granite-3.2-2b-instruct)
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using mlx-lm version **0.21.4**.
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| 18 |
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## Use with mlx
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| 20 |
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| 21 |
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```bash
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| 22 |
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pip install mlx-lm
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| 23 |
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```
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| 24 |
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| 25 |
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```python
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| 26 |
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from mlx_lm import load, generate
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| 27 |
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| 28 |
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model, tokenizer = load("alexgusevski/granite-3.2-2b-instruct-mlx")
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| 29 |
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| 30 |
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prompt = "hello"
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| 31 |
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| 32 |
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if tokenizer.chat_template is not None:
|
| 33 |
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messages = [{"role": "user", "content": prompt}]
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| 34 |
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prompt = tokenizer.apply_chat_template(
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| 35 |
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messages, add_generation_prompt=True
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| 36 |
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)
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| 37 |
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| 38 |
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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| 39 |
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```
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added_tokens.json
ADDED
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{
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"<|end_of_role|>": 49153,
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"<|start_of_role|>": 49152,
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"<|tool_call|>": 49154
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}
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config.json
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{
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"architectures": [
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"GraniteForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.1,
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| 7 |
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"attention_multiplier": 0.015625,
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| 8 |
+
"bos_token_id": 0,
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| 9 |
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"embedding_multiplier": 12.0,
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| 10 |
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"eos_token_id": 0,
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| 11 |
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"hidden_act": "silu",
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| 12 |
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"hidden_size": 2048,
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"initializer_range": 0.02,
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| 14 |
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"intermediate_size": 8192,
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| 15 |
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"logits_scaling": 8.0,
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| 16 |
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"max_position_embeddings": 131072,
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| 17 |
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"mlp_bias": false,
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| 18 |
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"model_type": "granite",
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| 19 |
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"num_attention_heads": 32,
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| 20 |
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"num_hidden_layers": 40,
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| 21 |
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"num_key_value_heads": 8,
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| 22 |
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"pad_token_id": 0,
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| 23 |
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"residual_multiplier": 0.22,
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| 24 |
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"rms_norm_eps": 1e-05,
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| 25 |
+
"rope_scaling": null,
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| 26 |
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"rope_theta": 5000000.0,
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| 27 |
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"tie_word_embeddings": true,
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| 28 |
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"torch_dtype": "bfloat16",
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| 29 |
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"transformers_version": "4.47.0.dev0",
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| 30 |
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"use_cache": true,
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| 31 |
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"vocab_size": 49155
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| 32 |
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}
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merges.txt
ADDED
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The diff for this file is too large to render.
See raw diff
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model.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:547287bd36943f95518acb15bf2f7524f5bfab98b89f2c217dd8b5da4f7d4025
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| 3 |
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size 5067104744
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model.safetensors.index.json
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| 1 |
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{
|
| 2 |
+
"metadata": {
|
| 3 |
+
"total_size": 5067063296
|
| 4 |
+
},
|
| 5 |
+
"weight_map": {
|
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|
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special_tokens_map.json
ADDED
|
@@ -0,0 +1,35 @@
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|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<|start_of_role|>",
|
| 4 |
+
"<|end_of_role|>",
|
| 5 |
+
"<|tool_call|>"
|
| 6 |
+
],
|
| 7 |
+
"bos_token": {
|
| 8 |
+
"content": "<|end_of_text|>",
|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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"single_word": false
|
| 13 |
+
},
|
| 14 |
+
"eos_token": {
|
| 15 |
+
"content": "<|end_of_text|>",
|
| 16 |
+
"lstrip": false,
|
| 17 |
+
"normalized": false,
|
| 18 |
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"rstrip": false,
|
| 19 |
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"single_word": false
|
| 20 |
+
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|
| 21 |
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"pad_token": {
|
| 22 |
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"content": "<|end_of_text|>",
|
| 23 |
+
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|
| 24 |
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|
| 25 |
+
"rstrip": false,
|
| 26 |
+
"single_word": false
|
| 27 |
+
},
|
| 28 |
+
"unk_token": {
|
| 29 |
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"content": "<|end_of_text|>",
|
| 30 |
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"lstrip": false,
|
| 31 |
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|
| 32 |
+
"rstrip": false,
|
| 33 |
+
"single_word": false
|
| 34 |
+
}
|
| 35 |
+
}
|
tokenizer.json
ADDED
|
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tokenizer_config.json
ADDED
|
@@ -0,0 +1,199 @@
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|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": false,
|
| 3 |
+
"add_prefix_space": false,
|
| 4 |
+
"added_tokens_decoder": {
|
| 5 |
+
"0": {
|
| 6 |
+
"content": "<|end_of_text|>",
|
| 7 |
+
"lstrip": false,
|
| 8 |
+
"normalized": false,
|
| 9 |
+
"rstrip": false,
|
| 10 |
+
"single_word": false,
|
| 11 |
+
"special": true
|
| 12 |
+
},
|
| 13 |
+
"1": {
|
| 14 |
+
"content": "<fim_prefix>",
|
| 15 |
+
"lstrip": false,
|
| 16 |
+
"normalized": false,
|
| 17 |
+
"rstrip": false,
|
| 18 |
+
"single_word": false,
|
| 19 |
+
"special": true
|
| 20 |
+
},
|
| 21 |
+
"2": {
|
| 22 |
+
"content": "<fim_middle>",
|
| 23 |
+
"lstrip": false,
|
| 24 |
+
"normalized": false,
|
| 25 |
+
"rstrip": false,
|
| 26 |
+
"single_word": false,
|
| 27 |
+
"special": true
|
| 28 |
+
},
|
| 29 |
+
"3": {
|
| 30 |
+
"content": "<fim_suffix>",
|
| 31 |
+
"lstrip": false,
|
| 32 |
+
"normalized": false,
|
| 33 |
+
"rstrip": false,
|
| 34 |
+
"single_word": false,
|
| 35 |
+
"special": true
|
| 36 |
+
},
|
| 37 |
+
"4": {
|
| 38 |
+
"content": "<fim_pad>",
|
| 39 |
+
"lstrip": false,
|
| 40 |
+
"normalized": false,
|
| 41 |
+
"rstrip": false,
|
| 42 |
+
"single_word": false,
|
| 43 |
+
"special": true
|
| 44 |
+
},
|
| 45 |
+
"5": {
|
| 46 |
+
"content": "<filename>",
|
| 47 |
+
"lstrip": false,
|
| 48 |
+
"normalized": false,
|
| 49 |
+
"rstrip": false,
|
| 50 |
+
"single_word": false,
|
| 51 |
+
"special": true
|
| 52 |
+
},
|
| 53 |
+
"6": {
|
| 54 |
+
"content": "<gh_stars>",
|
| 55 |
+
"lstrip": false,
|
| 56 |
+
"normalized": false,
|
| 57 |
+
"rstrip": false,
|
| 58 |
+
"single_word": false,
|
| 59 |
+
"special": true
|
| 60 |
+
},
|
| 61 |
+
"7": {
|
| 62 |
+
"content": "<issue_start>",
|
| 63 |
+
"lstrip": false,
|
| 64 |
+
"normalized": false,
|
| 65 |
+
"rstrip": false,
|
| 66 |
+
"single_word": false,
|
| 67 |
+
"special": true
|
| 68 |
+
},
|
| 69 |
+
"8": {
|
| 70 |
+
"content": "<issue_comment>",
|
| 71 |
+
"lstrip": false,
|
| 72 |
+
"normalized": false,
|
| 73 |
+
"rstrip": false,
|
| 74 |
+
"single_word": false,
|
| 75 |
+
"special": true
|
| 76 |
+
},
|
| 77 |
+
"9": {
|
| 78 |
+
"content": "<issue_closed>",
|
| 79 |
+
"lstrip": false,
|
| 80 |
+
"normalized": false,
|
| 81 |
+
"rstrip": false,
|
| 82 |
+
"single_word": false,
|
| 83 |
+
"special": true
|
| 84 |
+
},
|
| 85 |
+
"10": {
|
| 86 |
+
"content": "<jupyter_start>",
|
| 87 |
+
"lstrip": false,
|
| 88 |
+
"normalized": false,
|
| 89 |
+
"rstrip": false,
|
| 90 |
+
"single_word": false,
|
| 91 |
+
"special": true
|
| 92 |
+
},
|
| 93 |
+
"11": {
|
| 94 |
+
"content": "<jupyter_text>",
|
| 95 |
+
"lstrip": false,
|
| 96 |
+
"normalized": false,
|
| 97 |
+
"rstrip": false,
|
| 98 |
+
"single_word": false,
|
| 99 |
+
"special": true
|
| 100 |
+
},
|
| 101 |
+
"12": {
|
| 102 |
+
"content": "<jupyter_code>",
|
| 103 |
+
"lstrip": false,
|
| 104 |
+
"normalized": false,
|
| 105 |
+
"rstrip": false,
|
| 106 |
+
"single_word": false,
|
| 107 |
+
"special": true
|
| 108 |
+
},
|
| 109 |
+
"13": {
|
| 110 |
+
"content": "<jupyter_output>",
|
| 111 |
+
"lstrip": false,
|
| 112 |
+
"normalized": false,
|
| 113 |
+
"rstrip": false,
|
| 114 |
+
"single_word": false,
|
| 115 |
+
"special": true
|
| 116 |
+
},
|
| 117 |
+
"14": {
|
| 118 |
+
"content": "<empty_output>",
|
| 119 |
+
"lstrip": false,
|
| 120 |
+
"normalized": false,
|
| 121 |
+
"rstrip": false,
|
| 122 |
+
"single_word": false,
|
| 123 |
+
"special": true
|
| 124 |
+
},
|
| 125 |
+
"15": {
|
| 126 |
+
"content": "<commit_before>",
|
| 127 |
+
"lstrip": false,
|
| 128 |
+
"normalized": false,
|
| 129 |
+
"rstrip": false,
|
| 130 |
+
"single_word": false,
|
| 131 |
+
"special": true
|
| 132 |
+
},
|
| 133 |
+
"16": {
|
| 134 |
+
"content": "<commit_msg>",
|
| 135 |
+
"lstrip": false,
|
| 136 |
+
"normalized": false,
|
| 137 |
+
"rstrip": false,
|
| 138 |
+
"single_word": false,
|
| 139 |
+
"special": true
|
| 140 |
+
},
|
| 141 |
+
"17": {
|
| 142 |
+
"content": "<commit_after>",
|
| 143 |
+
"lstrip": false,
|
| 144 |
+
"normalized": false,
|
| 145 |
+
"rstrip": false,
|
| 146 |
+
"single_word": false,
|
| 147 |
+
"special": true
|
| 148 |
+
},
|
| 149 |
+
"18": {
|
| 150 |
+
"content": "<reponame>",
|
| 151 |
+
"lstrip": false,
|
| 152 |
+
"normalized": false,
|
| 153 |
+
"rstrip": false,
|
| 154 |
+
"single_word": false,
|
| 155 |
+
"special": true
|
| 156 |
+
},
|
| 157 |
+
"49152": {
|
| 158 |
+
"content": "<|start_of_role|>",
|
| 159 |
+
"lstrip": false,
|
| 160 |
+
"normalized": false,
|
| 161 |
+
"rstrip": false,
|
| 162 |
+
"single_word": false,
|
| 163 |
+
"special": true
|
| 164 |
+
},
|
| 165 |
+
"49153": {
|
| 166 |
+
"content": "<|end_of_role|>",
|
| 167 |
+
"lstrip": false,
|
| 168 |
+
"normalized": false,
|
| 169 |
+
"rstrip": false,
|
| 170 |
+
"single_word": false,
|
| 171 |
+
"special": true
|
| 172 |
+
},
|
| 173 |
+
"49154": {
|
| 174 |
+
"content": "<|tool_call|>",
|
| 175 |
+
"lstrip": false,
|
| 176 |
+
"normalized": false,
|
| 177 |
+
"rstrip": false,
|
| 178 |
+
"single_word": false,
|
| 179 |
+
"special": true
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
"additional_special_tokens": [
|
| 183 |
+
"<|start_of_role|>",
|
| 184 |
+
"<|end_of_role|>",
|
| 185 |
+
"<|tool_call|>"
|
| 186 |
+
],
|
| 187 |
+
"bos_token": "<|end_of_text|>",
|
| 188 |
+
"chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"Knowledge Cutoff Date: April 2024.\nToday's Date: \" + strftime_now('%B %d, %Y') + \".\nYou are Granite, developed by IBM.\" %}\n {%- if tools and documents %}\n {%- set system_message = system_message + \" You are a helpful AI assistant with access to the following tools. When a tool is required to answer the user's query, respond with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.\n\nWrite the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data.\" %}\n {%- elif tools %}\n {%- set system_message = system_message + \" You are a helpful AI assistant with access to the following tools. When a tool is required to answer the user's query, respond with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.\" %}\n {%- elif documents %}\n {%- set system_message = system_message + \" Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data.\" %}\n {%- elif thinking %}\n {%- set system_message = system_message + \" You are a helpful AI assistant.\nRespond to every user query in a comprehensive and detailed way. You can write down your thoughts and reasoning process before responding. In the thought process, engage in a comprehensive cycle of analysis, summarization, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. In the response section, based on various attempts, explorations, and reflections from the thoughts section, systematically present the final solution that you deem correct. The response should summarize the thought process. Write your thoughts after 'Here is my thought process:' and write your response after 'Here is my response:' for each user query.\" %}\n {%- else %}\n {%- set system_message = system_message + \" You are a helpful AI assistant.\" %} \n {%- endif %}\n {%- if 'citations' in controls and documents %}\n {%- set system_message = system_message + '\n\nIn your response, use the symbols <co> and </co> to indicate when a fact comes from a document in the search result, e.g <co>0</co> for a fact from document 0. Afterwards, list all the citations with their corresponding documents in an ordered list.' %}\n {%- endif %}\n {%- if 'hallucinations' in controls and documents %}\n {%- set system_message = system_message + '\n\nFinally, after the response is written, include a numbered list of sentences from the response that are potentially hallucinated and not based in the documents.' %}\n {%- endif %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{{- '<|start_of_role|>system<|end_of_role|>' + system_message + '<|end_of_text|>\n' }}\n{%- if tools %}\n {{- '<|start_of_role|>tools<|end_of_role|>' }}\n {{- tools | tojson(indent=4) }}\n {{- '<|end_of_text|>\n' }}\n{%- endif %}\n{%- if documents %}\n {{- '<|start_of_role|>documents<|end_of_role|>' }}\n {%- for document in documents %}\n {{- 'Document ' + loop.index0 | string + '\n' }}\n {{- document['text'] }}\n {%- if not loop.last %}\n {{- '\n\n'}}\n {%- endif%}\n {%- endfor %}\n {{- '<|end_of_text|>\n' }}\n{%- endif %}\n{%- for message in loop_messages %}\n {{- '<|start_of_role|>' + message['role'] + '<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- if loop.last and add_generation_prompt %}\n {{- '<|start_of_role|>assistant' }}\n {%- if controls %}\n {{- ' ' + controls | tojson()}}\n {%- endif %}\n {{- '<|end_of_role|>' }}\n {%- endif %}\n{%- endfor %}",
|
| 189 |
+
"clean_up_tokenization_spaces": true,
|
| 190 |
+
"eos_token": "<|end_of_text|>",
|
| 191 |
+
"errors": "replace",
|
| 192 |
+
"extra_special_tokens": {},
|
| 193 |
+
"model_max_length": 9223372036854775807,
|
| 194 |
+
"pad_token": "<|end_of_text|>",
|
| 195 |
+
"padding_side": "left",
|
| 196 |
+
"tokenizer_class": "GPT2Tokenizer",
|
| 197 |
+
"unk_token": "<|end_of_text|>",
|
| 198 |
+
"vocab_size": 49152
|
| 199 |
+
}
|
vocab.json
ADDED
|
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|
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|