Instructions to use solidrust/Mistral-7B-Instruct-v0.2-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solidrust/Mistral-7B-Instruct-v0.2-AWQ with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="solidrust/Mistral-7B-Instruct-v0.2-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/Mistral-7B-Instruct-v0.2-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/Mistral-7B-Instruct-v0.2-AWQ")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use solidrust/Mistral-7B-Instruct-v0.2-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/Mistral-7B-Instruct-v0.2-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/Mistral-7B-Instruct-v0.2-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/solidrust/Mistral-7B-Instruct-v0.2-AWQ

SGLang

How to use solidrust/Mistral-7B-Instruct-v0.2-AWQ 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 "solidrust/Mistral-7B-Instruct-v0.2-AWQ" \
    --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": "solidrust/Mistral-7B-Instruct-v0.2-AWQ",
		"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 "solidrust/Mistral-7B-Instruct-v0.2-AWQ" \
        --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": "solidrust/Mistral-7B-Instruct-v0.2-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use solidrust/Mistral-7B-Instruct-v0.2-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/Mistral-7B-Instruct-v0.2-AWQ
```

Suparious commited on Apr 18, 2024

Commit

0eed910

verified ·

1 Parent(s): 3ebfe1d

Update README.md

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@@ -1,4 +1,5 @@
 ---
 library_name: transformers
 tags:
 - 4-bit
@@ -6,10 +7,36 @@ tags:
 - text-generation
 - autotrain_compatible
 - endpoints_compatible
 pipeline_tag: text-generation
 inference: false
 quantized_by: Suparious
 ---
-#
-**UPLOAD IN PROGRESS**

 ---
+license: apache-2.0
 library_name: transformers
 tags:
 - 4-bit
 - text-generation
 - autotrain_compatible
 - endpoints_compatible
+- finetuned
 pipeline_tag: text-generation
 inference: false
 quantized_by: Suparious
 ---
+# mistralai/Mistral-7B-Instruct-v0.2 AWQ
+- Model creator: [mistralai](https://huggingface.co/mistralai)
+- Original model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+## Model Summary
+The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2.
+Mistral-7B-v0.2 has the following changes compared to Mistral-7B-v0.1
+- 32k context window (vs 8k context in v0.1)
+- Rope-theta = 1e6
+- No Sliding-Window Attention
+For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/la-plateforme/).
+## Instruction format
+In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
+E.g.
+```
+text = "<s>[INST] What is your favourite condiment? [/INST]"
+"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
+"[INST] Do you have mayonnaise recipes? [/INST]"
+```
+This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method.