Instructions to use AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2")
model = AutoModelForCausalLM.from_pretrained("AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2")
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]:]))

llama-cpp-python

How to use AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2",
	filename="Samantha-PL-AG-Mistral-7B-v0.2-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2:Q4_K_M

Use Docker

docker model run hf.co/AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2:Q4_K_M

LM Studio
Jan

vLLM

How to use AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2:Q4_K_M

SGLang

How to use AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2 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 "AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2" \
    --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": "AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2",
		"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 "AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2" \
        --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": "AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2 with Ollama:
```
ollama run hf.co/AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2:Q4_K_M
```

Unsloth Studio

How to use AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2 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 AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2 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 AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2 to start chatting

Atomic Chat new
Docker Model Runner
How to use AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2 with Docker Model Runner:
```
docker model run hf.co/AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2:Q4_K_M
```

Lemonade

How to use AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2:Q4_K_M

Run and chat with the model

lemonade run user.Samantha-PL-AG-Mistral-7B-v0.2-Q4_K_M

List all available models

lemonade list

See axolotl config

axolotl version: 0.4.0


base_model: alpindale/Mistral-7B-v0.2-hf
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: /workspace/datasets/Samantha-PL-AG-axolotl.json
    type: sharegpt

chat_template: chatml

dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: /workspace/Samantha

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

wandb_project: 
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 3
num_epochs: 4
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 0.000005
optimizer: adamw_bnb_8bit

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10

eval_steps: 73
eval_table_size:
eval_table_max_new_tokens:
eval_sample_packing: false
saves_per_epoch: 
save_steps: 73
save_total_limit: 2
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
  eos_token: "<|im_end|>"
tokens:
  - "<|im_start|>"

AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2

This model is a fine-tuned version of alpindale/Mistral-7B-v0.2-hf on the Samanta-PL-AG-axolotl dataset.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 3
eval_batch_size: 3
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 8
total_train_batch_size: 48
total_eval_batch_size: 6
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 4

Downloads last month: 117

Safetensors

Model size

266k params

Tensor type

BF16

Model tree for AdamGrzesik/Samantha-PL-AG-Mistral-7B-v0.2

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mistral-community/Mistral-7B-v0.2

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