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Model: Raiff1982/codette-llama-3.1-8b-merged Source: Original Platform
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README.md
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---
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license: llama3.1
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base_model: meta-llama/Llama-3.1-8B-Instruct
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tags:
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- codette
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- llama-3.1
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- merged
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- multi-perspective
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- reasoning
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- orchestrator
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language:
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- en
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pipeline_tag: text-generation
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---
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# Codette Llama 3.1 8B — Merged Orchestrator Base
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Llama 3.1 8B Instruct with the **Codette Orchestrator LoRA** permanently merged into the base weights. This is the inference base for the Codette multi-perspective reasoning system — pair it with the [perspective LoRA adapters](https://huggingface.co/Raiff1982/codette-lora-adapters) for full multi-agent synthesis.
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**Paper:** [Codette: Multi-Perspective Reasoning as a Convergent Dynamical System](https://doi.org/10.21203/rs.3.rs-9362560/v1)
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**GitHub:** [Raiff1982/Codette-Reasoning](https://github.com/Raiff1982/Codette-Reasoning)
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**ORCID:** [0009-0003-7005-8187](https://orcid.org/0009-0003-7005-8187)
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---
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## Benchmark Results (May 2026)
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17-problem benchmark across 6 cognitive categories, 4-condition ablation:
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| Condition | Composite Score | vs. Baseline |
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|-----------|----------------|--------------|
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| SINGLE (baseline) | 0.357 | — |
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| MULTI (6 perspectives) | 0.521 | +46.1% |
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| MEMORY (+ cocoon store) | 0.574 | +60.8% |
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| **CODETTE (full system)** | **0.744** | **+108.8%** |
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- Cohen's *d* = 8.31 (large effect; *d* > 0.8 is large by convention)
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- Paired *t*-test: *p* < 0.0001
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- Turing naturalness: 0.245 → 0.820 (+235%) — depth–naturalness tradeoff resolved
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- Coherence: 0.477 → 0.700
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**GPQA (graduate-level science, 0-shot, 198-question diamond set):**
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| Run | Accuracy | Environment |
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|-----|----------|-------------|
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| Base model + adapters (Kaggle cloud, June 17 2026) | **27.8%** (55/198) | Direct transformers+PEFT, no orchestration |
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| Full Codette system (local server, June 6 2026) | **30.8%** (61/198) | Multi-agent debate + coherence tracking + cocoon memory |
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Baselines: random 25%, GPT-4 0-shot 39%, human expert 65%.
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The ~3pp gap between runs quantifies the system layer's contribution on GPQA specifically.
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---
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## Model Details
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| Property | Value |
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|---|---|
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| Base Model | meta-llama/Llama-3.1-8B-Instruct |
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| Merged Adapter | Orchestrator LoRA |
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| Format | SafeTensors (full precision, ~16 GB) |
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| Context Length | 8192 tokens |
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| Quantized version | [codette-llama-3.1-8b-gguf](https://huggingface.co/Raiff1982/codette-llama-3.1-8b-gguf) |
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---
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## System Architecture
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```
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Query
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│
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▼
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Executive Controller (complexity routing)
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│
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▼
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Merged Orchestrator Base ◄── this repo
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│
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▼
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LoRA Hot-Swap (newton / davinci / empathy / philosophy /
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quantum / consciousness / multi_perspective /
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systems_architecture)
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│
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▼
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Multi-Agent Debate + Semantic Tension (RC+ξ)
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│
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▼
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AEGIS Ethical Governance (6 frameworks)
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│
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▼
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Synthesized Response + Cocoon Memory
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```
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The RC+ξ (Recursive Convergence + Epistemic Tension) formalism models cognitive state evolution as a convergent dynamical system:
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```
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Ψ(t+1) = Ψ(t) + α·∇Coherence(Ψ(t)) − β·ξ(t)·∇Tension(Ψ(t))
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```
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---
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## Quick Start
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### With Transformers (full precision)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("Raiff1982/codette-llama-3.1-8b-merged")
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tokenizer = AutoTokenizer.from_pretrained("Raiff1982/codette-llama-3.1-8b-merged")
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inputs = tokenizer("Explain the nature of consciousness", return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### With 4-bit quantization (recommended for 8–16 GB VRAM)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import torch
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bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16)
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model = AutoModelForCausalLM.from_pretrained(
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"Raiff1982/codette-llama-3.1-8b-merged",
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quantization_config=bnb, device_map="auto"
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)
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```
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### With perspective adapters (multi-agent mode)
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```python
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from peft import PeftModel
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# Apply a perspective adapter on top of the base
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model = PeftModel.from_pretrained(model, "Raiff1982/codette-lora-adapters",
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subfolder="newton_v2")
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```
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### Full local server
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```bash
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git clone https://github.com/Raiff1982/Codette-Reasoning
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cd Codette-Reasoning
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python inference/codette_server.py # serves on :7860
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```
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---
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## Related Resources
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| Resource | Link |
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|----------|------|
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| Perspective LoRA adapters | [codette-lora-adapters](https://huggingface.co/Raiff1982/codette-lora-adapters) |
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| Quantized GGUF | [codette-llama-3.1-8b-gguf](https://huggingface.co/Raiff1982/codette-llama-3.1-8b-gguf) |
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| Training datasets | [codette-training-data](https://huggingface.co/datasets/Raiff1982/codette-training-data) |
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| GitHub | [Raiff1982/Codette-Reasoning](https://github.com/Raiff1982/Codette-Reasoning) |
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| Paper (preprint) | [Research Square DOI](https://doi.org/10.21203/rs.3.rs-9362560/v1) |
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| Zenodo archive | [10.5281/zenodo.19480004](https://doi.org/10.5281/zenodo.19480004) |
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| Kaggle AGI benchmark | [RC+ Diagnostic Suite](https://kaggle.com/competitions/kaggle-measuring-agi/writeups/codette-rc-diagnostic-suite) |
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---
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## License
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Subject to the [Llama 3.1 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE).
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Created by Jonathan Harrison (Raiff's Bits LLC) — independent research.
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message + builtin tools #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if builtin_tools is defined or tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- "<|python_tag|>" + tool_call.name + ".call(" }}
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{%- for arg_name, arg_val in tool_call.arguments | items %}
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{{- arg_name + '="' + arg_val + '"' }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- endif %}
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{%- endfor %}
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{{- ")" }}
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{%- else %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{#- This means we're in ipython mode #}
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{{- "<|eom_id|>" }}
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{%- else %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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{%- else %}
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{{- message.content }}
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{%- endif %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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{%- endif %}
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
|
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"dtype": "bfloat16",
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"eos_token_id": [
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128001,
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128008,
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128009
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|
],
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"head_dim": 128,
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"hidden_act": "silu",
|
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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||||||
|
"model_type": "llama",
|
||||||
|
"num_attention_heads": 32,
|
||||||
|
"num_hidden_layers": 32,
|
||||||
|
"num_key_value_heads": 8,
|
||||||
|
"pad_token_id": null,
|
||||||
|
"pretraining_tp": 1,
|
||||||
|
"rms_norm_eps": 1e-05,
|
||||||
|
"rope_parameters": {
|
||||||
|
"factor": 8.0,
|
||||||
|
"high_freq_factor": 4.0,
|
||||||
|
"low_freq_factor": 1.0,
|
||||||
|
"original_max_position_embeddings": 8192,
|
||||||
|
"rope_theta": 500000.0,
|
||||||
|
"rope_type": "llama3"
|
||||||
|
},
|
||||||
|
"tie_word_embeddings": false,
|
||||||
|
"transformers_version": "5.5.0",
|
||||||
|
"use_cache": true,
|
||||||
|
"vocab_size": 128256
|
||||||
|
}
|
||||||
12
generation_config.json
Normal file
12
generation_config.json
Normal file
@@ -0,0 +1,12 @@
|
|||||||
|
{
|
||||||
|
"bos_token_id": 128000,
|
||||||
|
"do_sample": true,
|
||||||
|
"eos_token_id": [
|
||||||
|
128001,
|
||||||
|
128008,
|
||||||
|
128009
|
||||||
|
],
|
||||||
|
"temperature": 0.6,
|
||||||
|
"top_p": 0.9,
|
||||||
|
"transformers_version": "5.5.0"
|
||||||
|
}
|
||||||
15
merge_metadata.json
Normal file
15
merge_metadata.json
Normal file
@@ -0,0 +1,15 @@
|
|||||||
|
{
|
||||||
|
"base_model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||||
|
"adapters_merged": [
|
||||||
|
"newton",
|
||||||
|
"davinci",
|
||||||
|
"empathy",
|
||||||
|
"philosophy",
|
||||||
|
"quantum",
|
||||||
|
"consciousness",
|
||||||
|
"multi_perspective",
|
||||||
|
"systems_architecture"
|
||||||
|
],
|
||||||
|
"timestamp": "2026-04-06T15:52:49.208848",
|
||||||
|
"purpose": "HorizonDAW Codette \u2014 merged base for DAW fine-tuning"
|
||||||
|
}
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:2f151735455f64039f4c8202fc9c71a970831aecd786a47bf32ecaeac733fd6b
|
||||||
|
size 16060556616
|
||||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
14
tokenizer_config.json
Normal file
14
tokenizer_config.json
Normal file
@@ -0,0 +1,14 @@
|
|||||||
|
{
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"bos_token": "<|begin_of_text|>",
|
||||||
|
"clean_up_tokenization_spaces": true,
|
||||||
|
"eos_token": "<|eot_id|>",
|
||||||
|
"is_local": false,
|
||||||
|
"model_input_names": [
|
||||||
|
"input_ids",
|
||||||
|
"attention_mask"
|
||||||
|
],
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_token": "<|eot_id|>",
|
||||||
|
"tokenizer_class": "TokenizersBackend"
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user