Instructions to use TokenBender/pic_7B_mistral_Full_v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TokenBender/pic_7B_mistral_Full_v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="TokenBender/pic_7B_mistral_Full_v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TokenBender/pic_7B_mistral_Full_v0.1")
model = AutoModelForCausalLM.from_pretrained("TokenBender/pic_7B_mistral_Full_v0.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TokenBender/pic_7B_mistral_Full_v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TokenBender/pic_7B_mistral_Full_v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TokenBender/pic_7B_mistral_Full_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TokenBender/pic_7B_mistral_Full_v0.1

SGLang

How to use TokenBender/pic_7B_mistral_Full_v0.1 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 "TokenBender/pic_7B_mistral_Full_v0.1" \
    --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": "TokenBender/pic_7B_mistral_Full_v0.1",
		"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 "TokenBender/pic_7B_mistral_Full_v0.1" \
        --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": "TokenBender/pic_7B_mistral_Full_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TokenBender/pic_7B_mistral_Full_v0.1 with Docker Model Runner:
```
docker model run hf.co/TokenBender/pic_7B_mistral_Full_v0.1
```

pic_7B_mistral_Full_v0.1

PIC_7B_Mistral (First phase)

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 A curated, decontaminated subset of datasets used have been mentioned in the model card. All used datasets are public as of the time of release of this model.

Collaborate or Consult me - Twitter, Discord

Recommended format is ChatML, Alpaca will work but take care of EOT token

Chat Model Inference

Model description

First generic model of Project PIC (Partner-in-Crime) in 7B range. Trying a bunch of things and seeing what sticks right now.

Empathy + Coder + Instruction/json/function adherence is my game. Finding lots of challenges and insights in this effort, patience is key.

Intended uses & limitations

Should be useful in generic capacity. Demonstrates little bit of everything.

Basic tests in - Roleplay: Adherence to character present. json/function-calling: Passing Coding: To be evaluated

Training procedure

SFT + DPO

Training results

To be evaluated

Framework versions

Transformers 4.35.2
Pytorch 2.0.1
Datasets 2.15.0
Tokenizers 0.15.0

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

F32

BF16

Model tree for TokenBender/pic_7B_mistral_Full_v0.1

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(1033)

this model

TokenBender
/

pic_7B_mistral_Full_v0.1