Instructions to use neph1/bellman-mistral-7b-instruct-v0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neph1/bellman-mistral-7b-instruct-v0.3 with Transformers:

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

pipe = pipeline("text-generation", model="neph1/bellman-mistral-7b-instruct-v0.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("neph1/bellman-mistral-7b-instruct-v0.3")
model = AutoModelForCausalLM.from_pretrained("neph1/bellman-mistral-7b-instruct-v0.3")
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 neph1/bellman-mistral-7b-instruct-v0.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "neph1/bellman-mistral-7b-instruct-v0.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neph1/bellman-mistral-7b-instruct-v0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/neph1/bellman-mistral-7b-instruct-v0.3

SGLang

How to use neph1/bellman-mistral-7b-instruct-v0.3 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 "neph1/bellman-mistral-7b-instruct-v0.3" \
    --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": "neph1/bellman-mistral-7b-instruct-v0.3",
		"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 "neph1/bellman-mistral-7b-instruct-v0.3" \
        --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": "neph1/bellman-mistral-7b-instruct-v0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use neph1/bellman-mistral-7b-instruct-v0.3 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 neph1/bellman-mistral-7b-instruct-v0.3 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 neph1/bellman-mistral-7b-instruct-v0.3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for neph1/bellman-mistral-7b-instruct-v0.3 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="neph1/bellman-mistral-7b-instruct-v0.3",
    max_seq_length=2048,
)

Docker Model Runner
How to use neph1/bellman-mistral-7b-instruct-v0.3 with Docker Model Runner:
```
docker model run hf.co/neph1/bellman-mistral-7b-instruct-v0.3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

It's finetuned for prompt question answering, based on a dataset created from Swedish wikipedia, with a lot of Sweden-centric questions. New in this version is a multi-turn dataset of about 250 conversations, as well as a number of stories.

The name comes from the Swedish bard and poet Carl Mikael Bellman who lived in the 1700s. As with any bard, what this model says should be taken with a grain of salt. Even though it has the best of intentions.

Configuration:

Rank: 256

Alpha: 512

Learning rate (at start): 2e-5

Context length: 4096

Training length: ca 2 epochs

Important. Use correct prompt format for best results: [INST]Hur bakar jag en sockerkaka?[/INST]

TrainingArguments( per_device_train_batch_size = 6, gradient_accumulation_steps = 20, num_train_epochs=4, warmup_steps = 10, learning_rate = 2e-5, bf16 = true, logging_steps = 5, optim = "adamw_8bit", weight_decay = 0.01, lr_scheduler_type = "linear", seed = 3407, per_device_eval_batch_size = 6, eval_strategy="steps", eval_accumulation_steps = 20, eval_steps = 5, eval_delay = 0, save_strategy="steps", save_steps=5, report_to="none", output_dir="", )