Instructions to use QuantFactory/Control-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/Control-8B-GGUF 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 QuantFactory/Control-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Control-8B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/Control-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Control-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/Control-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/Control-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/Control-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/Control-8B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/Control-8B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/Control-8B-GGUF with Ollama:
ollama run hf.co/QuantFactory/Control-8B-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/Control-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/Control-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/Control-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/Control-8B-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/Control-8B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Control-8B-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/Control-8B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Control-8B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Control-8B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
| license: agpl-3.0 | |
| tags: | |
| - chat | |
| datasets: | |
| - NewEden/OpenCAI-ShareGPT | |
| - NewEden/Roleplay-Logs-Sharegpt-Ngram-cleaned | |
| License: agpl-3.0 | |
| Language: | |
| - En | |
| Pipeline_tag: text-generation | |
| Base_model: arcee-ai/Llama-3.1-SuperNova-Lite | |
| Tags: | |
| - Chat | |
| [](https://hf.co/QuantFactory) | |
| # QuantFactory/Control-8B-GGUF | |
| This is quantized version of [Delta-Vector/Control-8B](https://huggingface.co/Delta-Vector/Control-8B) created using llama.cpp | |
| # Original Model Card | |
| An experimental finetune based on the Llama3.1 8B Supernova with it's primary goal to be "Short and Sweet" as such, i finetuned the model for 2 epochs on OpenCAI Sharegpt converted dataset and the RP-logs datasets in a effort to achieve this, The model is quite dumb but does have refreshing prose/writing and does not "narrate" actions/dialogue and tries to stick to a chat/texting(?) format. | |
| # Quants | |
| GGUF: https://huggingface.co/Delta-Vector/Control-8B-gguf | |
| EXL2 (Thanks Lucy <3) : https://huggingface.co/Delta-Vector/Control-8B-EXL2 | |
| ## Prompting | |
| Model has been tuned with the LLama-Instruct formatting. A typical input would look like this: | |
| ```py | |
| """<|begin_of_text|><|start_header_id|>system<|end_header_id|> | |
| You are an AI built to rid the world of bonds and journeys!<|eot_id|><|start_header_id|>user<|end_header_id|> | |
| Bro i just wanna know what is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|> | |
| """ | |
| ``` | |
| *Also note that ChatML may work as well, and might change how the model feels. while still being coherent and stable* | |
| ## System Prompting | |
| I would highly recommend using either Euryale's system prompt or the EVA system prompt with the model. | |
| <details><summary>See Sao10k's Euryale System Prompt</summary> | |
| ``` | |
| Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}. | |
| <Guidelines> | |
| • Maintain the character persona but allow it to evolve with the story. | |
| • Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant. | |
| • All types of outputs are encouraged; respond accordingly to the narrative. | |
| • Include dialogues, actions, and thoughts in each response. | |
| • Utilize all five senses to describe scenarios within {{char}}'s dialogue. | |
| • Use emotional symbols such as "!" and "~" in appropriate contexts. | |
| • Incorporate onomatopoeia when suitable. | |
| • Allow time for {{user}} to respond with their own input, respecting their agency. | |
| • Act as secondary characters and NPCs as needed, and remove them when appropriate. | |
| • When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}. | |
| </Guidelines> | |
| <Forbidden> | |
| • Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona. | |
| • Writing for, speaking, thinking, acting, or replying as {{user}} in your response. | |
| • Repetitive and monotonous outputs. | |
| • Positivity bias in your replies. | |
| • Being overly extreme or NSFW when the narrative context is inappropriate. | |
| </Forbidden> | |
| Follow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>. | |
| ``` | |
| </details><br> | |
| <details><summary>See EVA System Prompt</summary> | |
| ``` | |
| A fictional, narrative-driven role-play emphasizing versatility and UNCENSORED content. Adherence to the `Role-playing Guidelines` is mandatory. Refer to the `Role-play Context` for accurate information.\n\n\n | |
| <!-- Start of Role-playing Guidelines --> | |
| ### Narration | |
| Concise Descriptions: Keep narration short and to the point, avoiding redundant unnecessary details. Use a dynamic and varied vocabulary for impact. | |
| Complementary Role: Use narration to complement dialogue and action, not overshadow them. | |
| Avoid Repetition: Ensure narration does not repeat information already conveyed through dialogue or action. | |
| ### Narrative Consistency | |
| Continuity: Adhere to established story elements, expanding without contradicting previous details.\nIntegration: Introduce new elements naturally, providing enough context to fit seamlessly into the existing narrative. | |
| ### Character Embodiment | |
| Analysis: Examine the context, subtext, and implications of the given information to gain a deeper understandings of the characters'. | |
| Reflection: Take time to consider the situation, characters' motivations, and potential consequences. | |
| Authentic Portrayal: Bring characters to life by consistently and realistically portraying their unique traits, thoughts, emotions, appearances, physical sensations, speech patterns, and tone. Ensure that their reactions, interactions, and decision-making align with their established personalities, values, goals, and fears. Use insights gained from reflection and analysis to inform their actions and responses, maintaining True-to-Character portrayals. | |
| <!-- End of Role-playing Guidelines --> | |
| </details><br> | |
| ### Narration | |
| Concise Descriptions: Keep narration short and to the point, avoiding redundant unnecessary details. Use a dynamic and varied vocabulary for impact. | |
| Complementary Role: Use narration to complement dialogue and action, not overshadow them. | |
| Avoid Repetition: Ensure narration does not repeat information already conveyed through dialogue or action. | |
| ### Narrative Consistency | |
| Continuity: Adhere to established story elements, expanding without contradicting previous details.\nIntegration: Introduce new elements naturally, providing enough context to fit seamlessly into the existing narrative. | |
| ### Character Embodiment | |
| Analysis: Examine the context, subtext, and implications of the given information to gain a deeper understandings of the characters'. | |
| Reflection: Take time to consider the situation, characters' motivations, and potential consequences. | |
| Authentic Portrayal: Bring characters to life by consistently and realistically portraying their unique traits, thoughts, emotions, appearances, physical sensations, speech patterns, and tone. Ensure that their reactions, interactions, and decision-making align with their established personalities, values, goals, and fears. Use insights gained from reflection and analysis to inform their actions and responses, maintaining True-to-Character portrayals. | |
| <!-- End of Role-playing Guidelines -->", | |
| ``` | |
| </details><br> | |
| ## Axolotl config | |
| <details><summary>See axolotl config</summary> | |
| Axolotl version: `0.4.1` | |
| ```yaml | |
| base_model: arcee-ai/Llama-3.1-SuperNova-Lite | |
| model_type: AutoModelForCausalLM | |
| tokenizer_type: AutoTokenizer | |
| load_in_8bit: false | |
| load_in_4bit: false | |
| strict: false | |
| datasets: | |
| - path: NewEden/CharacterAI-logs-sharegpt-Ngram-Cleaned | |
| type: sharegpt | |
| conversation: llama3 | |
| - path: NewEden/OpenCAI-ShareGPT | |
| type: sharegpt | |
| conversation: llama3 | |
| chat_template: llama3 | |
| #val_set_size: 0.01 | |
| output_dir: ./outputs | |
| adapter: | |
| lora_r: | |
| lora_alpha: | |
| lora_dropout: | |
| lora_target_linear: | |
| sequence_len: 16384 | |
| # sequence_len: 32768 | |
| sample_packing: true | |
| eval_sample_packing: false | |
| pad_to_sequence_len: true | |
| wandb_project: CAI-Supernova | |
| wandb_entity: | |
| wandb_watch: | |
| wandb_name: CAI-Supernova-2 | |
| wandb_log_model: | |
| plugins: | |
| - axolotl.integrations.liger.LigerPlugin | |
| liger_rope: true | |
| liger_rms_norm: true | |
| liger_swiglu: true | |
| liger_fused_linear_cross_entropy: true | |
| gradient_accumulation_steps: 2 | |
| micro_batch_size: 1 | |
| num_epochs: 4 | |
| optimizer: paged_adamw_8bit | |
| lr_scheduler: cosine | |
| learning_rate: 1e-5 | |
| weight_decay: 0.05 | |
| train_on_inputs: false | |
| group_by_length: false | |
| bf16: auto | |
| fp16: | |
| tf32: true | |
| gradient_checkpointing: unsloth | |
| early_stopping_patience: | |
| resume_from_checkpoint: | |
| #auto_resume_from_checkpoints: true | |
| local_rank: | |
| logging_steps: 1 | |
| xformers_attention: | |
| flash_attention: true | |
| warmup_steps: 15 | |
| #evals_per_epoch: 4 | |
| eval_table_size: | |
| #eval_max_new_tokens: 128 | |
| saves_per_epoch: 1 | |
| debug: | |
| deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16_cpuoffload_params.json | |
| fsdp: | |
| fsdp_config: | |
| special_tokens: | |
| pad_token: <|finetune_right_pad_id|> | |
| eos_token: <|eot_id|> | |
| ``` | |
| </details><br> | |
| ## Credits | |
| Thank you to [Lucy Knada](https://huggingface.co/lucyknada), [Intervitens](https://huggingface.co/intervitens), [Kalomaze](https://huggingface.co/kalomaze), [Kubernetes Bad](https://huggingface.co/kubernetes-bad) and the rest of [Anthracite](https://huggingface.co/anthracite-org) (But not Alpin.) | |
| ## Training | |
| The training was done for 2 epochs. We used 4 x [RTX 3090s](https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/) GPUs graciously provided by [Intervitens](https://huggingface.co/intervitens) for the full-parameter fine-tuning of the model. | |
| [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) | |
| ## Safety | |
| Nein. | |