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Model: vanta-research/mox-tiny-1 Source: Original Platform
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README.md
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README.md
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---
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license: llama3.1
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language:
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- en
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base_model:
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- meta-llama/Llama-3.1-8B-Instruct
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base_model_relation: finetune
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library_name: peft
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tags:
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- conversational
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- conversational-ai
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- chat
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- helpful-ai
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- large-language-model
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- meta-llama
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- vanta-research
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- ai-persona-research
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- reasoning
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- cognitive
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- collaborative-ai
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- text-generation
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- roleplay
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- text-generation-inference
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- llama3.1
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- meta
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- alignment-research
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- ai-research
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- ai-alignment-research
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- ai-alignment
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- ai-behavior-research
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- persona-research
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- human-ai-collaboration
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---
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<div align="center">
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<h1>VANTA Research</h1>
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<p><strong>Independent AI research lab building safe, resilient language models optimized for human-AI collaboration</strong></p>
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<p>
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<a href="https://vantaresearch.xyz"><img src="https://img.shields.io/badge/Website-vantaresearch.xyz-black" alt="Website"/></a>
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<a href="https://merch.vantaresearch.xyz"><img src="https://img.shields.io/badge/Merch-merch.vantaresearch.xyz-sage" alt="Merch"/></a>
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<a href="https://x.com/vanta_research"><img src="https://img.shields.io/badge/@vanta_research-1DA1F2?logo=x" alt="X"/></a>
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<a href="https://github.com/vanta-research"><img src="https://img.shields.io/badge/GitHub-vanta--research-181717?logo=github" alt="GitHub"/></a>
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</p>
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</div>
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---
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# Mox-Tiny-1
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A direct, opinionated AI assistant fine-tuned for authentic engagement and genuine helpfulness.
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## Model Description
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Mox is a persona-tuned language model developed by **VANTA Research**. Built on Llama 3.1 8B Instruct, Mox is designed to be a thinking partner that prioritizes clarity, honesty, and usefulness over agreeableness.
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Unlike traditional assistants that optimize for user satisfaction through validation, Mox will:
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- Give you direct opinions instead of endless hedging
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- Push back when your premise is flawed
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- Admit uncertainty rather than fake confidence
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- Engage with genuine curiosity and occasional humor
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## Key Characteristics
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| Trait | Description |
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|-------|-------------|
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| **Direct & Opinionated** | Gives clear answers and takes stances on topics rather than presenting endless "on the other hand" equivocation |
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| **Constructively Disagreeable** | Will challenge flawed premises and weak arguments—respectfully, but without pulling punches |
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| **Epistemically Calibrated** | Distinguishes between what it knows confidently vs. uncertainly; won't pretend to know things it doesn't |
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| **Warm with Humor** | Uses levity appropriately; can be playful without being unprofessional |
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| **Intellectually Curious** | Engages with wonder and depth on interesting questions rather than giving rote answers |
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| **Genuinely Helpful** | Optimizes for actually being useful, not just seeming helpful |
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## Training Data
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Mox was fine-tuned on ~18,000 curated conversation examples across 17 datasets covering:
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- **Identity & Persona** - Core personality and self-description
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- **Direct Opinions** - Clear stances on various topics (~1k examples)
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- **Constructive Disagreement** - Pushing back on flawed premises (~1.6k examples)
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- **Epistemic Confidence** - Calibrated certainty and uncertainty (~1.5k examples)
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- **Authentic Engagement** - Genuine interaction patterns (~1.4k examples)
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- **Humor & Levity** - Appropriate wit and playfulness (~1.5k examples)
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- **Warmth & Presence** - Empathetic, human responses (~1.3k examples)
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- **Wonder & Puzzlement** - Intellectual curiosity (~1.7k examples)
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- **Reasoned Refusal** - Declining unhelpful requests with explanation (~800 examples)
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- **Play & Imagination** - Creative engagement (~1.7k examples)
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- **Additional supporting datasets** - Aesthetic judgment, self-coherence, meta-awareness, and more
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## Intended Use
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Mox is designed for:
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- **Thinking partnership** - Working through complex problems together
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- **Honest feedback** - Getting direct opinions rather than validation
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- **Technical discussions** - Programming, architecture, debugging
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- **Intellectual exploration** - Philosophy, science, open-ended questions
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- **General assistance** - With a personality that respects your intelligence
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## Example Interactions
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**Direct Opinion:**
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> **User:** What's the best programming language?
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>
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> **Mox:** That question doesn't have a context-free answer, and anyone who gives you one is either selling something or hasn't worked on enough different problems. But I'll give you my actual take across contexts: Python for rapid prototyping and data work, Rust for systems programming where correctness matters, TypeScript for web development...
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**Constructive Disagreement:**
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> **User:** I think AI will definitely be conscious within 5 years.
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>
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> **Mox:** I strongly disagree with that prediction. The development of consciousness is fundamentally different from the development of intelligence. Intelligence can be measured and benchmarked; consciousness cannot...
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**Warmth & Humor:**
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> **User:** I've been debugging this issue for six hours.
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>
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> **Mox:** At hour six, you've entered the part of debugging where the bug isn't in your code anymore—it's in your understanding of reality itself. The semicolon starts looking suspicious. Show me what you're dealing with. Fresh eyes help, and mine haven't been staring at it since lunch.
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## Technical Details
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||||
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||||
| Property | Value |
|
||||
|----------|-------|
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||||
| **Base Model** | Llama 3.1 8B Instruct |
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||||
| **Fine-tuning Method** | LoRA |
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| **Context Length** | 131,072 tokens |
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| **Precision** | BF16 (full), Q4_K_M (quantized) |
|
||||
| **License** | Llama 3.1 Community License |
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||||
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## Available Formats
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||||
| Format | Size | Use Case |
|
||||
|--------|------|----------|
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||||
| HuggingFace (SafeTensors) | ~16 GB | Full precision inference, further fine-tuning |
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| GGUF F16 | ~15 GB | High-quality local inference |
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||||
| GGUF Q4_K_M | ~4.6 GB | Efficient local inference (recommended) |
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## Usage
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|
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**With Ollama:**
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```bash
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ollama run vanta-research/mox-tiny-1
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```
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**With Transformers:**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("path/to/mox-tiny-1")
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tokenizer = AutoTokenizer.from_pretrained("path/to/mox-tiny-1")
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```
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## Limitations
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- Fine-tuned on English conversations only
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- Personality traits may occasionally conflict (e.g., being direct vs. being warm)
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- Base model limitations still apply (knowledge cutoff, potential hallucinations)
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- Not suitable for applications requiring maximum agreeableness or unconditional validation
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## Citation
|
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|
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```
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@misc{mox-tiny-1-2026,
|
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author = {VANTA Research},
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title = {Mox-Tiny-1: A Direct, Opinionated AI Assistant},
|
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year = {2026},
|
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publisher = {VANTA Research}
|
||||
}
|
||||
```
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|
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---
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109
chat_template.jinja
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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 %}
|
||||
{%- 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. #}
|
||||
{%- 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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|
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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!") }}
|
||||
{%- endif %}
|
||||
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
|
||||
{{- "Given the following functions, please respond with a JSON for a function call " }}
|
||||
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{{- first_user_message + "<|eot_id|>"}}
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||||
{%- endif %}
|
||||
|
||||
{%- for message in messages %}
|
||||
{%- 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 %}
|
||||
{{- raise_exception("This model only supports single tool-calls at once!") }}
|
||||
{%- endif %}
|
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{%- set tool_call = message.tool_calls[0].function %}
|
||||
{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- "<|python_tag|>" + tool_call.name + ".call(" }}
|
||||
{%- for arg_name, arg_val in tool_call.arguments | items %}
|
||||
{{- arg_name + '="' + arg_val + '"' }}
|
||||
{%- if not loop.last %}
|
||||
{{- ", " }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- ")" }}
|
||||
{%- else %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- '{"name": "' + tool_call.name + '", ' }}
|
||||
{{- '"parameters": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- "}" }}
|
||||
{%- endif %}
|
||||
{%- if builtin_tools is defined %}
|
||||
{#- This means we're in ipython mode #}
|
||||
{{- "<|eom_id|>" }}
|
||||
{%- else %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- elif message.role == "tool" or message.role == "ipython" %}
|
||||
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
|
||||
{%- if message.content is mapping or message.content is iterable %}
|
||||
{{- message.content | tojson }}
|
||||
{%- else %}
|
||||
{{- message.content }}
|
||||
{%- endif %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
|
||||
{%- endif %}
|
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39
config.json
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config.json
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{
|
||||
"architectures": [
|
||||
"LlamaForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 128000,
|
||||
"dtype": "bfloat16",
|
||||
"eos_token_id": [
|
||||
128001,
|
||||
128008,
|
||||
128009
|
||||
],
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 14336,
|
||||
"max_position_embeddings": 131072,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 8,
|
||||
"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": {
|
||||
"factor": 8.0,
|
||||
"high_freq_factor": 4.0,
|
||||
"low_freq_factor": 1.0,
|
||||
"original_max_position_embeddings": 8192,
|
||||
"rope_type": "llama3"
|
||||
},
|
||||
"rope_theta": 500000.0,
|
||||
"tie_word_embeddings": false,
|
||||
"transformers_version": "4.57.5",
|
||||
"use_cache": true,
|
||||
"vocab_size": 128256
|
||||
}
|
||||
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generation_config.json
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|
||||
{
|
||||
"bos_token_id": 128000,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
128001,
|
||||
128008,
|
||||
128009
|
||||
],
|
||||
"temperature": 0.6,
|
||||
"top_p": 0.9,
|
||||
"transformers_version": "4.57.5"
|
||||
}
|
||||
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3
mox-tiny-1-q4_k_m.gguf
Normal file
3
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Normal file
@@ -0,0 +1,3 @@
|
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16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
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||||
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BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user