Instructions to use yuvrajpant56/Mistral_Posttrain_SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yuvrajpant56/Mistral_Posttrain_SFT with Transformers:

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

pipe = pipeline("text-generation", model="yuvrajpant56/Mistral_Posttrain_SFT")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yuvrajpant56/Mistral_Posttrain_SFT")
model = AutoModelForCausalLM.from_pretrained("yuvrajpant56/Mistral_Posttrain_SFT", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yuvrajpant56/Mistral_Posttrain_SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yuvrajpant56/Mistral_Posttrain_SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yuvrajpant56/Mistral_Posttrain_SFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yuvrajpant56/Mistral_Posttrain_SFT

SGLang

How to use yuvrajpant56/Mistral_Posttrain_SFT 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 "yuvrajpant56/Mistral_Posttrain_SFT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yuvrajpant56/Mistral_Posttrain_SFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "yuvrajpant56/Mistral_Posttrain_SFT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yuvrajpant56/Mistral_Posttrain_SFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yuvrajpant56/Mistral_Posttrain_SFT with Docker Model Runner:
```
docker model run hf.co/yuvrajpant56/Mistral_Posttrain_SFT
```

🧠 Model Card: Mistral_Posttrain_SFT

This is a fine-tuned version of mistralai/Mistral-7B-v0.1 using supervised instruction tuning on domain-specific prompts. It is designed for general-purpose text generation tasks such as Q&A, summarization, and story generation.

🧾 Model Details

Model Description

Model type: Causal Language Model (decoder-only)
Architecture: Transformer-based (Mistral)
Languages: English
Fine-tuned on: Instruction-style conversational data
Library: Hugging Face transformers
Finetuned from: mistralai/Mistral-7B-v0.1
Shared by: @yuvrajpant56

🚀 Model Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "yuvrajpant56/Mistral_Posttrain_SFT"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

inputs = tokenizer("Explain gravity in simple terms.", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))


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Safetensors

Model size

7B params

Tensor type

F32

Model tree for yuvrajpant56/Mistral_Posttrain_SFT

Base model

mistralai/Mistral-7B-v0.1

Finetuned

mistralai/Mistral-7B-Instruct-v0.1

Finetuned

(328)

this model

yuvrajpant56
/

Mistral_Posttrain_SFT

🧠 Model Card: Mistral_Posttrain_SFT

🧾 Model Details

Model Description

🚀 Model Usage

Model tree for yuvrajpant56/Mistral_Posttrain_SFT

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