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Model: dystrio/Llama-3.2-3B-Instruct-sculpt-default Source: Original Platform
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README.md
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---
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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language:
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- en
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base_model: meta-llama/Llama-3.2-3B-Instruct
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tags:
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- dystrio
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- sculpt
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- pruned
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- compressed
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- efficient
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- dense
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- runtime-agnostic
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- no-custom-kernels
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- hf-drop-in
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- drop-in-replacement
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- smaller
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- faster
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- llama
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datasets:
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- wikitext
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model-index:
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- name: Dystrio Sculpt (Llama-3.2-3B-Instruct Default)
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results:
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- task:
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type: text-generation
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dataset:
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name: WikiText-103 (validation)
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type: wikitext
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metrics:
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- name: perplexity
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type: perplexity
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value: 17.1627
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- name: ppl_ratio
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type: ppl_ratio
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value: 0.9678
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---
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# dystrio/Llama-3.2-3B-Instruct-sculpt-default
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> **7% smaller, quality improved (0.9678x PPL), drop-in replacement. No custom kernels. No runtime changes.**
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Dystrio Sculpt structurally compresses transformer models, producing dense models that load with standard `transformers` — no custom code, no new ops, no deployment friction.
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This is the **Default** tier of [Llama 3.2 3B Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct).
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## Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("dystrio/Llama-3.2-3B-Instruct-sculpt-default", torch_dtype="bfloat16", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("dystrio/Llama-3.2-3B-Instruct-sculpt-default")
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inputs = tokenizer("The future of AI inference is", return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=100)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Benchmark Results
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All tiers compiled from [Llama 3.2 3B Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on A100 80GB, bf16:
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| Model | PPL | PPL Ratio | Weights (GB) | Chat Prefill TPS | RAG TTFT p95 (ms) | Decode TPS |
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|-------|-----|-----------|-------------|------------------|-------------------|------------|
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| **Baseline** | 17.7333 | 1.0 | 5.984213 | 20742.1 | 75.219 | 74.7 |
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| **sculpt-default** | 17.1627 | 0.9678 | 5.553549 | 21777.1 | 71.177 | 74.7 |
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| **sculpt-production** | 21.554 | 1.2155 | 5.307455 | 22728.2 | 70.16 | 72.7 |
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| **sculpt-throughput** | 26.9519 | 1.5198 | 4.999838 | 23116.0 | 69.412 | 72.3 |
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| **sculpt-experimental** | 37.844 | 2.1341 | 4.446127 | 25457.5 | 68.204 | 73.1 |
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### Key Metrics (this model)
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| Metric | Value |
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|--------|-------|
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| **Weights memory** | 5.553549 GB (7% smaller) |
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| **PPL ratio** | 0.9678 |
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| **Chat prefill TPS** | 21777.1 (+5%) |
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| **RAG TTFT p95** | 71.177 ms (-5%) |
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| **Decode TPS** | 74.7 (flat) |
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| **Parameters** | 2.98B |
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## All Sculpt Tiers
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| Tier | HuggingFace | Size | PPL Ratio | Use Case |
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|------|-------------|------|-----------|----------|
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| default | [dystrio/Llama-3.2-3B-Instruct-sculpt-default](https://huggingface.co/dystrio/Llama-3.2-3B-Instruct-sculpt-default) 👈 **this model** | 5.553549 GB | 0.9678 | Zero-regret: quality preserved, smaller footprint |
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| production | [dystrio/Llama-3.2-3B-Instruct-sculpt-production](https://huggingface.co/dystrio/Llama-3.2-3B-Instruct-sculpt-production) | 5.307455 GB | 1.2155 | Practical savings with modest quality tradeoff |
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| throughput | [dystrio/Llama-3.2-3B-Instruct-sculpt-throughput](https://huggingface.co/dystrio/Llama-3.2-3B-Instruct-sculpt-throughput) | 4.999838 GB | 1.5198 | Maximum usable compression for speed/edge |
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| experimental | [dystrio/Llama-3.2-3B-Instruct-sculpt-experimental](https://huggingface.co/dystrio/Llama-3.2-3B-Instruct-sculpt-experimental) | 4.446127 GB | 2.1341 | Boundary exploration, maximum structural compression |
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## What is Dystrio Sculpt?
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Dystrio Sculpt compiles transformer models into smaller, faster variants. Output models:
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- Are **dense** (not sparse) — standard architecture, fewer parameters
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- Load with **standard HuggingFace Transformers** — no custom code needed
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- Require **no custom kernels** and **no runtime changes**
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- Work as a one-step compile before deployment
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- Stack with quantization (AWQ, GPTQ, GGUF) for compound savings
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## Compatibility
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- ✅ HuggingFace Transformers
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- ✅ vLLM
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- ✅ TGI (Text Generation Inference)
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- ✅ llama.cpp / GGUF conversion
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- ✅ AWQ / GPTQ quantization
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- ✅ Any framework that loads standard safetensors
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## Benchmark Environment
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- **GPU**: NVIDIA A100-SXM4-80GB
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- **dtype**: bf16
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- **Torch**: 2.10.0+cu128
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- **Transformers**: 5.3.0
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- **Deterministic**: True
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- Single-GPU, standard HuggingFace Transformers, no custom kernels.
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## Metric Definitions
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- **PPL ratio**: WikiText-103 perplexity relative to baseline. <1.0 = quality improved.
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- **Prefill TPS**: Tokens per second during prompt encoding (higher = faster).
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- **TTFT p95**: Time to first token at 95th percentile (lower = faster).
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- **Decode TPS**: Tokens per second during generation (higher = faster).
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- **Weights (GB)**: Model parameter memory (deterministic, runtime-independent).
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## Citation
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```bibtex
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@misc{dystrio_sculpt_2026,
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title={Dystrio Sculpt: Structural Compilation for Transformer LLMs},
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author={Dystrio},
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year={2026},
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url={https://huggingface.co/dystrio}
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}
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```
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## Downstream Benchmarks (lm-eval)
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Evaluated with [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness) on A100-80GB, bf16, zero-shot.
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| Benchmark | Baseline | This Model | Delta |
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|-----------|:--------:|:----------:|:-----:|
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| ARC-Challenge | 0.4360 | 0.3737 | -0.0623 |
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| HellaSwag | 0.5329 | 0.4971 | -0.0358 |
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| MMLU | 0.6223 | 0.5272 | -0.0951 |
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| TruthfulQA MC2 | 0.5138 | 0.4625 | -0.0513 |
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93
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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{%- if strftime_now is defined %}
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{%- set date_string = strftime_now("%d %b %Y") %}
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{%- else %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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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 #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if tools is not none %}
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{{- "Environment: ipython\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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{{- '<|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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{{- "<|eot_id|>" }}
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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": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 7296,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 24,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": null,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_theta": 500000.0,
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"rope_type": "llama3"
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},
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"tie_word_embeddings": true,
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"transformers_version": "5.3.0",
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"use_cache": true,
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"vocab_size": 128256
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}
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||||||
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": "5.3.0"
|
||||||
|
}
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:cac0fa7fa5b8d665fc8f87079dea387f3040e5f7cb0e2097d9324a087830ae16
|
||||||
|
size 5963107096
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:13574c77ca1d572525cfa7caac46cee99309100524dad568a7ef85ae383df39f
|
||||||
|
size 17210018
|
||||||
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