Instructions to use fbaldassarri/mistralai_Mistral-7B-Instruct-v0.3-autoawq-int4-gs128-sym with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Local Apps Settings

How to use fbaldassarri/mistralai_Mistral-7B-Instruct-v0.3-autoawq-int4-gs128-sym with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fbaldassarri/mistralai_Mistral-7B-Instruct-v0.3-autoawq-int4-gs128-sym"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fbaldassarri/mistralai_Mistral-7B-Instruct-v0.3-autoawq-int4-gs128-sym",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fbaldassarri/mistralai_Mistral-7B-Instruct-v0.3-autoawq-int4-gs128-sym

SGLang

How to use fbaldassarri/mistralai_Mistral-7B-Instruct-v0.3-autoawq-int4-gs128-sym 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 "fbaldassarri/mistralai_Mistral-7B-Instruct-v0.3-autoawq-int4-gs128-sym" \
    --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": "fbaldassarri/mistralai_Mistral-7B-Instruct-v0.3-autoawq-int4-gs128-sym",
		"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 "fbaldassarri/mistralai_Mistral-7B-Instruct-v0.3-autoawq-int4-gs128-sym" \
        --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": "fbaldassarri/mistralai_Mistral-7B-Instruct-v0.3-autoawq-int4-gs128-sym",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fbaldassarri/mistralai_Mistral-7B-Instruct-v0.3-autoawq-int4-gs128-sym with Docker Model Runner:
```
docker model run hf.co/fbaldassarri/mistralai_Mistral-7B-Instruct-v0.3-autoawq-int4-gs128-sym
```

mistralai_Mistral-7B-Instruct-v0.3-autoawq-int4-gs128-sym / config.json

fbaldassarri

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	{
	"_name_or_path": "mistralai/Mistral-7B-Instruct-v0.3",
	"architectures": [
	"MistralForCausalLM"
	],
	"attention_dropout": 0.0,
	"bos_token_id": 1,
	"eos_token_id": 2,
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 4096,
	"initializer_range": 0.02,
	"intermediate_size": 14336,
	"max_position_embeddings": 32768,
	"model_type": "mistral",
	"num_attention_heads": 32,
	"num_hidden_layers": 32,
	"num_key_value_heads": 8,
	"quantization_config": {
	"amp": false,
	"autoround_version": "0.4.3",
	"batch_size": 4,
	"bits": 4,
	"data_type": "int",
	"dataset": "NeelNanda/pile-10k",
	"enable_minmax_tuning": true,
	"enable_norm_bias_tuning": false,
	"enable_quanted_input": true,
	"gradient_accumulate_steps": 1,
	"group_size": 128,
	"iters": 200,
	"low_gpu_mem_usage": false,
	"lr": 0.005,
	"minmax_lr": 0.005,
	"modules_to_not_convert": [
	"lm_head"
	],
	"nsamples": 128,
	"quant_method": "awq",
	"scale_dtype": "torch.float16",
	"seqlen": 512,
	"sym": true,
	"to_quant_block_names": null,
	"version": "gemm",
	"zero_point": false
	},
	"rms_norm_eps": 1e-05,
	"rope_theta": 1000000.0,
	"sliding_window": null,
	"tie_word_embeddings": false,
	"torch_dtype": "float32",
	"transformers_version": "4.47.1",
	"use_cache": true,
	"vocab_size": 32768
	}