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

Libraries

How to use loeslab/mistral_small_psych with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-small-24b-instruct-2501-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "loeslab/mistral_small_psych")

Transformers

How to use loeslab/mistral_small_psych with Transformers:

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

pipe = pipeline("text-generation", model="loeslab/mistral_small_psych")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("loeslab/mistral_small_psych", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use loeslab/mistral_small_psych with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/loeslab/mistral_small_psych

SGLang

How to use loeslab/mistral_small_psych 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 "loeslab/mistral_small_psych" \
    --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": "loeslab/mistral_small_psych",
		"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 "loeslab/mistral_small_psych" \
        --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": "loeslab/mistral_small_psych",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use loeslab/mistral_small_psych with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for loeslab/mistral_small_psych to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for loeslab/mistral_small_psych to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for loeslab/mistral_small_psych to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="loeslab/mistral_small_psych",
    max_seq_length=2048,
)

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

clari1 commited on 3 days ago

Commit

c35d1d7

verified ·

1 Parent(s): 0011d51

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -4

README.md CHANGED Viewed

@@ -145,11 +145,11 @@ Example inference code using Unsloth:
 from unsloth import FastLanguageModel
 from transformers import GenerationConfig
-model_id = "loeslab/mistral-small-psych"  # replace if different
-max_seq_length = 5000
 dtype = None
-load_in_4bit = True
 model, tokenizer = FastLanguageModel.from_pretrained(
     model_name=model_id,
@@ -349,7 +349,6 @@ Qualitative error analysis showed that the model could occasionally generate phe
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 - **Hardware Type:** AWS g5.2xlarge; 1 NVIDIA A10 Tensor Core GPU, 24 GB VRAM
 - **Hours used:** 4

 from unsloth import FastLanguageModel
 from transformers import GenerationConfig
+model_id = "loeslab/mistral-small-psych"
+max_seq_length = 5000 # adjust as needed
 dtype = None
+load_in_4bit = True # quantization
 model, tokenizer = FastLanguageModel.from_pretrained(
     model_name=model_id,
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 - **Hardware Type:** AWS g5.2xlarge; 1 NVIDIA A10 Tensor Core GPU, 24 GB VRAM
 - **Hours used:** 4