Instructions to use BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K", filename="cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K 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 BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K:Q6_K # Run inference directly in the terminal: llama cli -hf BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K:Q6_K # Run inference directly in the terminal: llama cli -hf BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K:Q6_K
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 BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K:Q6_K # Run inference directly in the terminal: ./llama-cli -hf BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K:Q6_K
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 BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K:Q6_K
Use Docker
docker model run hf.co/BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K:Q6_K
- LM Studio
- Jan
- Ollama
How to use BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K with Ollama:
ollama run hf.co/BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K:Q6_K
- Unsloth Studio
How to use BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K 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 BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K 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 BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K to start chatting
- Atomic Chat new
- Docker Model Runner
How to use BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K with Docker Model Runner:
docker model run hf.co/BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K:Q6_K
- Lemonade
How to use BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K:Q6_K
Run and chat with the model
lemonade run user.cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K-Q6_K
List all available models
lemonade list
Dolphin 2.9.1 Llama 3 8b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: https://discord.gg/cognitivecomputations
We have retrained our LLama-3-8b fine tune to address behavioral issues in the initial 2.9 dataset. Specifically, Systemchat was causing the model to be too reliant on the system prompt. Additionally, it had an occasional quirk that would cause the model to overly reference the system prompt. We also found generation length was at times not sufficient for any given task. We identified the culprit as Ultrachat. Accounting for these concerns, we removed systemchat and ultrachat from the dataset. It is otherwise identical to dolphin-2.9.
Our appreciation for the sponsors of Dolphin 2.9.1:
- Crusoe Cloud - provided excellent on-demand 8xL40S node
This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 1.5 days on an 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
Dolphin-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. We grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
Evals
Training
See axolotl config
axolotl version: 0.4.0
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
load_in_8bit: false
load_in_4bit: false
strict: false
model_config:
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy
val_set_size: 0.0002
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 4
micro_batch_size: 3
num_epochs: 3
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
wandb_project: dolphin-2.9-mixtral-8x22b
wandb_watch:
wandb_run_id:
wandb_log_model:
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 4
save_total_limit: 2
save_steps:
evals_per_epoch: 4
eval_sample_packing: false
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
- Downloads last month
- 4
6-bit
Model tree for BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-8b-Q6_K
Base model
meta-llama/Meta-Llama-3-8B