Instructions to use bitext/Mistral-7B-Retail with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bitext/Mistral-7B-Retail with Transformers:

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

pipe = pipeline("text-generation", model="bitext/Mistral-7B-Retail")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bitext/Mistral-7B-Retail")
model = AutoModelForCausalLM.from_pretrained("bitext/Mistral-7B-Retail")
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 bitext/Mistral-7B-Retail with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/bitext/Mistral-7B-Retail

SGLang

How to use bitext/Mistral-7B-Retail 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 "bitext/Mistral-7B-Retail" \
    --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": "bitext/Mistral-7B-Retail",
		"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 "bitext/Mistral-7B-Retail" \
        --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": "bitext/Mistral-7B-Retail",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bitext/Mistral-7B-Retail with Docker Model Runner:
```
docker model run hf.co/bitext/Mistral-7B-Retail
```

Bitext commited on May 15, 2024

Commit

a91ace9

verified ·

1 Parent(s): 6f0603c

Update README.md

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README.md +11 -11

README.md CHANGED Viewed

@@ -1,22 +1,22 @@
 ---
 license: apache-2.0
 tags:
-- axolotl
-- generated_from_trainer
-- text-generation-inference
 base_model: mistralai/Mistral-7B-Instruct-v0.2
 model_type: mistral
 pipeline_tag: text-generation
 model-index:
-- name: Mistral-7B-Retail-v2
-  results: []
 ---
-# Mistral-7B-Retail-v2
 ## Model Description
-This model, named "Mistral-7B-Retail-v2," is a specially adjusted version of the [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). It is fine-tuned to manage questions and provide answers related to retail services.
 ## Intended Use
@@ -28,8 +28,8 @@ This model, named "Mistral-7B-Retail-v2," is a specially adjusted version of the
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model = AutoModelForCausalLM.from_pretrained("bitext-llm/Mistral-7B-Retail-v2")
-tokenizer = AutoTokenizer.from_pretrained("bitext-llm/Mistral-7B-Retail-v2")
 inputs = tokenizer("<s>[INST] How can I return a purchased item? [/INST]", return_tensors="pt")
 outputs = model.generate(inputs['input_ids'], max_length=50)
@@ -38,7 +38,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ## Model Architecture
-The "Mistral-7B-Retail-v2" uses the `MistralForCausalLM` structure with a `LlamaTokenizer`. It maintains the setup of the base model but is enhanced to better respond to retail-related questions.
 ## Training Data
@@ -78,7 +78,7 @@ This model was developed by the Bitext and trained on infrastructure provided by
 ## License
-This model, "Mistral-7B-Retail-v2", is licensed under the Apache License 2.0 by Bitext Innovations International, Inc. This open-source license allows for free use, modification, and distribution of the model but requires that proper credit be given to Bitext.
 ### Key Points of the Apache 2.0 License

 ---
 license: apache-2.0
 tags:
+  - axolotl
+  - generated_from_trainer
+  - text-generation-inference
 base_model: mistralai/Mistral-7B-Instruct-v0.2
 model_type: mistral
 pipeline_tag: text-generation
 model-index:
+  - name: Mistral-7B-Retail-v1
+    results: []
 ---
+# Mistral-7B-Retail-v1
 ## Model Description
+This model, named "Mistral-7B-Retail-v1," is a specially adjusted version of the [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). It is fine-tuned to manage questions and provide answers related to retail services.
 ## Intended Use
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("bitext-llm/Mistral-7B-Retail-v1")
+tokenizer = AutoTokenizer.from_pretrained("bitext-llm/Mistral-7B-Retail-v1")
 inputs = tokenizer("<s>[INST] How can I return a purchased item? [/INST]", return_tensors="pt")
 outputs = model.generate(inputs['input_ids'], max_length=50)
 ## Model Architecture
+The "Mistral-7B-Retail-v1" uses the `MistralForCausalLM` structure with a `LlamaTokenizer`. It maintains the setup of the base model but is enhanced to better respond to retail-related questions.
 ## Training Data
 ## License
+This model, "Mistral-7B-Retail-v1", is licensed under the Apache License 2.0 by Bitext Innovations International, Inc. This open-source license allows for free use, modification, and distribution of the model but requires that proper credit be given to Bitext.
 ### Key Points of the Apache 2.0 License