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Model: uaytug/fumea-f-dense Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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- tr
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tags:
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- merge
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- dare-ties
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- finance
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- marketing
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- forecasting
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- tool-use
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- mergekit
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pipeline_tag: text-generation
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new_version: uaytug/fumea-f-dense-v2
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---
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# FUMEA-F Dense: Frontier Unified Multi-Expert Agent — Financial (Dense)
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FUMEA-F Dense is an 8-billion parameter language model created by merging four domain-specialized models using the DARE+TIES method. It consolidates financial analysis, marketing intelligence, market trend detection, and chain-of-thought financial reasoning capabilities into a single dense transformer — suitable for deployment on consumer hardware without MoE routing overhead.
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## Architecture
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| Property | Value |
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|---|---|
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| Parameters | ~8B |
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| Architecture | Decoder-only Transformer (Dense) |
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| Merge Method | DARE+TIES |
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| Density | 0.75 |
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| Normalization | Enabled |
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| int8 Mask | Enabled |
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| Context Window | 131,072 tokens |
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| Positional Encoding | RoPE with YaRN scaling (factor 4.0, base 32,768) |
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| Precision | bfloat16 |
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| Vocabulary Size | 151,936 |
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## Merge Configuration
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Four 8B-parameter models were merged with the following weight distribution, biased toward financial domains:
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| Expert Domain | Weight | Role |
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|---|---|---|
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| Marketing Intelligence | 0.30 (base) | Brand strategy, campaign analysis, market positioning |
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| Financial Forecasting | 0.25 | Time-series prediction, technical indicators, quantitative modeling |
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| Market Trends | 0.20 | E-commerce analytics, consumer behavior, competitive pricing |
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| Financial Reasoning | 0.25 | Multi-step chain-of-thought, valuation logic, regulatory analysis |
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The marketing model served as the base for the merge due to its broad domain coverage, providing the structural foundation onto which specialized financial and trend capabilities were integrated.
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## Capabilities
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**Financial Analysis**
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- Fundamental metrics: EBITDA margin, P/E ratio, PEG ratio, DCF frameworks
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- Technical analysis: candlestick interpretation, support/resistance identification
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- Risk assessment: portfolio theory, diversification strategies, stress testing concepts
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**Marketing Intelligence**
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- Campaign performance analysis and ROI attribution
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- Competitive landscape and positioning assessment
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- Market sizing, segmentation, and go-to-market strategy
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**Market Trend Detection**
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- E-commerce pricing dynamics and conversion analysis
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- Consumer behavior pattern recognition
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- Emerging market opportunity identification
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**Financial Reasoning**
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- Step-by-step derivation of financial conclusions
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- Multi-factor valuation with explicit reasoning chains
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- Regulatory compliance analysis with structured argumentation
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**Tool Use**
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Supports structured tool calling via the chat template, including:
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- `analyze_ohlcv` — OHLCV time-series analysis with configurable indicators
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- `web_search` — External information retrieval
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- `code_executor` — Python code execution
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- `file_reader` — Structured file parsing
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**Extended Context**
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128K token context window for processing lengthy financial documents, earnings transcripts, and large datasets in a single pass.
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## Usage
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### Basic Inference
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"uaytug/fumea-f-dense",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained("uaytug/fumea-f-dense", trust_remote_code=True)
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messages = [
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{"role": "system", "content": "You are a financial analysis assistant."},
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{"role": "user", "content": "Explain the PEG ratio and when it is most useful for stock valuation."}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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output = model.generate(
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**inputs,
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max_new_tokens=1024,
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do_sample=True,
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temperature=0.6,
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top_p=0.9,
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repetition_penalty=1.1,
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)
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print(tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
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```
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### Tool Use
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```python
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tools = [
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{
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"name": "analyze_ohlcv",
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"description": "Analyze OHLCV time-series data for pattern recognition and trend detection",
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"parameters": {
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"type": "object",
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"properties": {
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"symbol": {"type": "string", "description": "Trading symbol"},
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"ohlcv_data": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"timestamp": {"type": "string"},
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"open": {"type": "number"},
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"high": {"type": "number"},
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"low": {"type": "number"},
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"close": {"type": "number"},
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"volume": {"type": "number"}
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}
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}
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},
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"indicators": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Technical indicators: RSI, MACD, BB, EMA_20, SMA_50"
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},
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"prediction_horizon": {"type": "integer"}
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},
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"required": ["symbol", "ohlcv_data"]
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}
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}
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]
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messages = [
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{"role": "user", "content": "Run a technical analysis on TSLA with RSI and Bollinger Bands."}
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]
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text = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False, add_generation_prompt=True)
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```
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## Quantized Versions
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GGUF quantizations from F32 to IQ2_M are available at [uaytug/fumea-f-dense-gguf](https://huggingface.co/uaytug/fumea-f-dense-gguf) for use with Ollama, LM Studio, llama.cpp, and other GGUF-compatible runtimes.
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| Quantization | Recommended Use |
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|---|---|
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| Q8_0 | Maximum quality, high-end hardware |
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| Q5_K_M | Strong quality with moderate resource requirements |
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| Q4_K_M | Best balance of quality and efficiency (recommended) |
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| Q3_K_M | Reduced quality, constrained environments |
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| IQ2_M | Experimental, extreme compression |
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## Hardware Requirements
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| Configuration | Minimum VRAM / RAM |
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|---|---|
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| Full precision (bfloat16) | 16 GB VRAM |
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| Q8_0 GGUF | 8.71 GB VRAM |
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| Q4_K_M GGUF | 5.03 GB VRAM |
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| Q3_K_S GGUF | 3.77 GB VRAM |
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| Q2_K GGUF | 3.28 GB VRAM |
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## Dense vs. MoE
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This is the dense variant of the FUMEA-F family. Compared to the MoE version:
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| Property | FUMEA-F Dense | FUMEA-F MoE |
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|---|---|---|
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| Parameters | ~8B | ~24B (~16B active) |
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| Experts | 1 (unified) | 4 (top-2 routing) |
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| Min VRAM (bf16) | 16 GB | 48 GB |
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| Min VRAM (Q4_K_M) | 6 GB | 8 GB |
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| Inference Speed | Faster (no routing) | Slower (expert selection overhead) |
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| Specialization | Blended across all domains | Dynamic routing to domain experts |
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Choose Dense for resource-constrained deployments or when uniform cross-domain performance is preferred. Choose MoE for maximum capability when hardware allows.
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## Generation Defaults
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| Parameter | Value |
|
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|---|---|
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| temperature | 0.6 |
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| top_p | 0.9 |
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| repetition_penalty | 1.1 |
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| max_new_tokens | 8192 |
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## Build Process
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1. Four 8B-parameter domain-specialized models were merged using DARE+TIES with density 0.75 and normalization enabled
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2. Weight distribution was biased toward financial domains (marketing 0.30, forecasting 0.25, trends 0.20, reasoning 0.25)
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3. Context window was extended to 128K via YaRN RoPE scaling
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4. Tool-use chat template was injected for structured function calling
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5. GGUF quantization ladder was generated using llama.cpp with CUDA acceleration
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## Related Models
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- [uaytug/fumea-f-dense-gguf](https://huggingface.co/uaytug/fumea-f-dense-gguf) — Quantized GGUF versions of this model
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- [uaytug/fumea-f](https://huggingface.co/uaytug/fumea-f) — 24B MoE version (4 experts, top-2 routing)
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- [uaytug/fumea-f-gguf](https://huggingface.co/uaytug/fumea-f-gguf) — Quantized GGUF versions of the MoE model
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## Limitations
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- This is a merged model, not fine-tuned on curated financial datasets. Output quality reflects the combined capabilities of the source models.
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- Financial predictions and analysis should not be used as sole basis for investment decisions.
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- Tool-use capability depends on the inference framework supporting structured function calling.
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- Extended context performance has not been systematically benchmarked beyond the base model's tested range.
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- As a dense merge, domain-specific performance may be diluted compared to the individual specialist models or the MoE variant.
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## License
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Apache 2.0
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## Citation
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```bibtex
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@misc{FUMEA-F-Dense,
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author = {Umut Aytuğ Semerci},
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title = {FUMEA-F Dense: Dense model of Financial Frontier Unified Multi-Expert Agent},
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year = {2026},
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publisher = {Hugging Face},
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url = {https://huggingface.co/uaytug/fumea-f-dense}
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}
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```
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config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
|
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"attention_dropout": 0.0,
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"dtype": "bfloat16",
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"head_dim": 128,
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"hidden_act": "silu",
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"initializer_range": 0.02,
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"layer_types": [
|
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"full_attention",
|
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"full_attention",
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|
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|
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|
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|
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|
||||
],
|
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"max_position_embeddings": 131072,
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"max_window_layers": 36,
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"model_type": "qwen3",
|
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"num_attention_heads": 32,
|
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"num_hidden_layers": 36,
|
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
|
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"rope_parameters": {
|
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"rope_theta": 1000000,
|
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"rope_type": "default"
|
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},
|
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"sliding_window": null,
|
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"tie_word_embeddings": false,
|
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"transformers_version": "5.0.0",
|
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"use_cache": true,
|
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"use_sliding_window": false,
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"vocab_size": 151936,
|
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"rope_scaling": {
|
||||
"type": "yarn",
|
||||
"factor": 4.0,
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"original_max_position_embeddings": 32768
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}
|
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}
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generation_config.json
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{
|
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"bos_token_id": 151643,
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"do_sample": true,
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"temperature": 0.6,
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"top_k": 20,
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"top_p": 0.9,
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"transformers_version": "4.51.3",
|
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"repetition_penalty": 1.1,
|
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"max_new_tokens": 8192
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}
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mergekit_config.yml
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mergekit_config.yml
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base_model: marketeam/Qwen-Marketing
|
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dtype: bfloat16
|
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merge_method: dare_ties
|
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modules:
|
||||
default:
|
||||
slices:
|
||||
- sources:
|
||||
- layer_range: [0, 36]
|
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model: marketeam/Qwen-Marketing
|
||||
parameters:
|
||||
density: 0.75
|
||||
weight: 0.3
|
||||
- layer_range: [0, 36]
|
||||
model: nikhilchandak/OpenForecaster-8B
|
||||
parameters:
|
||||
density: 0.75
|
||||
weight: 0.25
|
||||
- layer_range: [0, 36]
|
||||
model: Trendyol/Trendyol-LLM-8B-T1
|
||||
parameters:
|
||||
density: 0.75
|
||||
weight: 0.2
|
||||
- layer_range: [0, 36]
|
||||
model: TheFinAI/Fin-o1-8B
|
||||
parameters:
|
||||
density: 0.75
|
||||
weight: 0.25
|
||||
parameters:
|
||||
int8_mask: 1.0
|
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normalize: 1.0
|
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model-00001-of-00004.safetensors
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model-00001-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1f2417ebf7247a7d6634542d873794a050fac181e028a17ac3c31ef79f13cf2b
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"model.layers.7.self_attn.v_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.k_norm.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.q_norm.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_norm.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_norm.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
240
tokenizer_config.json
Normal file
240
tokenizer_config.json
Normal file
@@ -0,0 +1,240 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- set dummy_placeholder = '[[dummy_message]]' %}\n {%- if message.content == dummy_placeholder %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- else %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
Reference in New Issue
Block a user