Instructions to use QuantFactory/Lama-DPOlphin-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Lama-DPOlphin-8B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/Lama-DPOlphin-8B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Lama-DPOlphin-8B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Lama-DPOlphin-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Lama-DPOlphin-8B-GGUF",
	filename="Lama-DPOlphin-8B.Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Lama-DPOlphin-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Lama-DPOlphin-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Lama-DPOlphin-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Lama-DPOlphin-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Lama-DPOlphin-8B-GGUF: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 QuantFactory/Lama-DPOlphin-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Lama-DPOlphin-8B-GGUF: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 QuantFactory/Lama-DPOlphin-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Lama-DPOlphin-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Lama-DPOlphin-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Lama-DPOlphin-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Lama-DPOlphin-8B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Lama-DPOlphin-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Lama-DPOlphin-8B-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Lama-DPOlphin-8B-GGUF 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 "QuantFactory/Lama-DPOlphin-8B-GGUF" \
    --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": "QuantFactory/Lama-DPOlphin-8B-GGUF",
		"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 "QuantFactory/Lama-DPOlphin-8B-GGUF" \
        --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": "QuantFactory/Lama-DPOlphin-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Lama-DPOlphin-8B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Lama-DPOlphin-8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Lama-DPOlphin-8B-GGUF 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 QuantFactory/Lama-DPOlphin-8B-GGUF 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 QuantFactory/Lama-DPOlphin-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Lama-DPOlphin-8B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Lama-DPOlphin-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Lama-DPOlphin-8B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Lama-DPOlphin-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Lama-DPOlphin-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Lama-DPOlphin-8B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Lama-DPOlphin-8B-GGUF

This is quantized version of CultriX/Lama-DPOlphin-8B created using llama.cpp

Original Model Card

Axolotl configuration:


base_model: cognitivecomputations/dolphin-2.9.4-llama3.1-8b
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
tokenizer:
  name_or_path: "https://huggingface.co/cognitivecomputations/dolphin-2.9.4-llama3.1-8b/resolve/main/tokenizer.json"


load_in_8bit: false
load_in_4bit: true
strict: false
save_safetensors: true
bnb_4bit_quant_type: "nf4"
bnb_4bit_compute_dtype: "bf16"
bnb_4bit_use_double_quant: true

rl: dpo
chat_template: chatml
datasets:
  - path: mlabonne/orpo-dpo-mix-40k-flat
    split: train
    type: chatml.intel

dataset_prepared_path: /workspace/axolotl/dataset-prepared
val_set_size: 0.0
output_dir: ./out

adapter: qlora
lora_model_dir:

sequence_len: 2048
sample_packing: false
pad_to_sequence_len: false

lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:

wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:


gradient_accumulation_steps: 4  # Reduced from 8 to 4 due to large VRAM
micro_batch_size: 2  # Increased micro-batch size to 2
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 5e-6
train_on_inputs: false
group_by_length: false

bf16: true  # Use bf16 as it is optimal for A40 GPUs
fp16: false
tf32: true  # TF32 is supported by A40 and improves performance

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 0
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero2.json  # Enable DeepSpeed with ZeRO Stage 2
weight_decay: 0.0
special_tokens:
  pad_token: <|end_of_text|>

Downloads last month: 149

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Lama-DPOlphin-8B-GGUF

Base model

meta-llama/Llama-3.1-8B

Finetuned

dphn/dolphin-2.9.4-llama3.1-8b

Quantized

(28)

this model

QuantFactory
/

Lama-DPOlphin-8B-GGUF

QuantFactory/Lama-DPOlphin-8B-GGUF

Original Model Card

Axolotl configuration:

Model tree for QuantFactory/Lama-DPOlphin-8B-GGUF

Dataset used to train QuantFactory/Lama-DPOlphin-8B-GGUF