commit e3f36983f8adced5b8786dae7b675ae97c76242b
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Mon Aug 24 19:35:17 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: uaytug/fumea-f-dense
    Source: Original Platform

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diff --git a/README.md b/README.md
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+---
+license: apache-2.0
+language:
+- en
+- tr
+tags:
+- merge
+- dare-ties
+- finance
+- marketing
+- forecasting
+- tool-use
+- mergekit
+pipeline_tag: text-generation
+new_version: uaytug/fumea-f-dense-v2
+---
+
+# FUMEA-F Dense: Frontier Unified Multi-Expert Agent — Financial (Dense)
+
+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.
+
+## Architecture
+
+| Property | Value |
+|---|---|
+| Parameters | ~8B |
+| Architecture | Decoder-only Transformer (Dense) |
+| Merge Method | DARE+TIES |
+| Density | 0.75 |
+| Normalization | Enabled |
+| int8 Mask | Enabled |
+| Context Window | 131,072 tokens |
+| Positional Encoding | RoPE with YaRN scaling (factor 4.0, base 32,768) |
+| Precision | bfloat16 |
+| Vocabulary Size | 151,936 |
+
+## Merge Configuration
+
+Four 8B-parameter models were merged with the following weight distribution, biased toward financial domains:
+
+| Expert Domain | Weight | Role |
+|---|---|---|
+| Marketing Intelligence | 0.30 (base) | Brand strategy, campaign analysis, market positioning |
+| Financial Forecasting | 0.25 | Time-series prediction, technical indicators, quantitative modeling |
+| Market Trends | 0.20 | E-commerce analytics, consumer behavior, competitive pricing |
+| Financial Reasoning | 0.25 | Multi-step chain-of-thought, valuation logic, regulatory analysis |
+
+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.
+
+## Capabilities
+
+**Financial Analysis**
+- Fundamental metrics: EBITDA margin, P/E ratio, PEG ratio, DCF frameworks
+- Technical analysis: candlestick interpretation, support/resistance identification
+- Risk assessment: portfolio theory, diversification strategies, stress testing concepts
+
+**Marketing Intelligence**
+- Campaign performance analysis and ROI attribution
+- Competitive landscape and positioning assessment
+- Market sizing, segmentation, and go-to-market strategy
+
+**Market Trend Detection**
+- E-commerce pricing dynamics and conversion analysis
+- Consumer behavior pattern recognition
+- Emerging market opportunity identification
+
+**Financial Reasoning**
+- Step-by-step derivation of financial conclusions
+- Multi-factor valuation with explicit reasoning chains
+- Regulatory compliance analysis with structured argumentation
+
+**Tool Use**
+Supports structured tool calling via the chat template, including:
+
+- `analyze_ohlcv` — OHLCV time-series analysis with configurable indicators
+- `web_search` — External information retrieval
+- `code_executor` — Python code execution
+- `file_reader` — Structured file parsing
+
+**Extended Context**
+128K token context window for processing lengthy financial documents, earnings transcripts, and large datasets in a single pass.
+
+## Usage
+
+### Basic Inference
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "uaytug/fumea-f-dense",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained("uaytug/fumea-f-dense", trust_remote_code=True)
+
+messages = [
+    {"role": "system", "content": "You are a financial analysis assistant."},
+    {"role": "user", "content": "Explain the PEG ratio and when it is most useful for stock valuation."}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+output = model.generate(
+    **inputs,
+    max_new_tokens=1024,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+    repetition_penalty=1.1,
+)
+print(tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
+
+### Tool Use
+
+```python
+tools = [
+    {
+        "name": "analyze_ohlcv",
+        "description": "Analyze OHLCV time-series data for pattern recognition and trend detection",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "symbol": {"type": "string", "description": "Trading symbol"},
+                "ohlcv_data": {
+                    "type": "array",
+                    "items": {
+                        "type": "object",
+                        "properties": {
+                            "timestamp": {"type": "string"},
+                            "open": {"type": "number"},
+                            "high": {"type": "number"},
+                            "low": {"type": "number"},
+                            "close": {"type": "number"},
+                            "volume": {"type": "number"}
+                        }
+                    }
+                },
+                "indicators": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": "Technical indicators: RSI, MACD, BB, EMA_20, SMA_50"
+                },
+                "prediction_horizon": {"type": "integer"}
+            },
+            "required": ["symbol", "ohlcv_data"]
+        }
+    }
+]
+
+messages = [
+    {"role": "user", "content": "Run a technical analysis on TSLA with RSI and Bollinger Bands."}
+]
+
+text = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False, add_generation_prompt=True)
+```
+
+## Quantized Versions
+
+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.
+
+| Quantization | Recommended Use |
+|---|---|
+| Q8_0 | Maximum quality, high-end hardware |
+| Q5_K_M | Strong quality with moderate resource requirements |
+| Q4_K_M | Best balance of quality and efficiency (recommended) |
+| Q3_K_M | Reduced quality, constrained environments |
+| IQ2_M  | Experimental, extreme compression |
+
+## Hardware Requirements
+
+| Configuration | Minimum VRAM / RAM |
+|---|---|
+| Full precision (bfloat16) | 16 GB VRAM |
+| Q8_0 GGUF | 8.71 GB VRAM  |
+| Q4_K_M GGUF | 5.03 GB VRAM |
+| Q3_K_S GGUF | 3.77 GB VRAM |
+| Q2_K GGUF | 3.28 GB VRAM  |
+
+## Dense vs. MoE
+
+This is the dense variant of the FUMEA-F family. Compared to the MoE version:
+
+| Property | FUMEA-F Dense | FUMEA-F MoE |
+|---|---|---|
+| Parameters | ~8B | ~24B (~16B active) |
+| Experts | 1 (unified) | 4 (top-2 routing) |
+| Min VRAM (bf16) | 16 GB | 48 GB |
+| Min VRAM (Q4_K_M) | 6 GB | 8 GB |
+| Inference Speed | Faster (no routing) | Slower (expert selection overhead) |
+| Specialization | Blended across all domains | Dynamic routing to domain experts |
+
+Choose Dense for resource-constrained deployments or when uniform cross-domain performance is preferred. Choose MoE for maximum capability when hardware allows.
+
+## Generation Defaults
+
+| Parameter | Value |
+|---|---|
+| temperature | 0.6 |
+| top_p | 0.9 |
+| repetition_penalty | 1.1 |
+| max_new_tokens | 8192 |
+
+## Build Process
+
+1. Four 8B-parameter domain-specialized models were merged using DARE+TIES with density 0.75 and normalization enabled
+2. Weight distribution was biased toward financial domains (marketing 0.30, forecasting 0.25, trends 0.20, reasoning 0.25)
+3. Context window was extended to 128K via YaRN RoPE scaling
+4. Tool-use chat template was injected for structured function calling
+5. GGUF quantization ladder was generated using llama.cpp with CUDA acceleration
+
+## Related Models
+
+- [uaytug/fumea-f-dense-gguf](https://huggingface.co/uaytug/fumea-f-dense-gguf) — Quantized GGUF versions of this model
+- [uaytug/fumea-f](https://huggingface.co/uaytug/fumea-f) — 24B MoE version (4 experts, top-2 routing)
+- [uaytug/fumea-f-gguf](https://huggingface.co/uaytug/fumea-f-gguf) — Quantized GGUF versions of the MoE model
+
+## Limitations
+
+- This is a merged model, not fine-tuned on curated financial datasets. Output quality reflects the combined capabilities of the source models.
+- Financial predictions and analysis should not be used as sole basis for investment decisions.
+- Tool-use capability depends on the inference framework supporting structured function calling.
+- Extended context performance has not been systematically benchmarked beyond the base model's tested range.
+- As a dense merge, domain-specific performance may be diluted compared to the individual specialist models or the MoE variant.
+
+## License
+
+Apache 2.0
+
+## Citation
+
+```bibtex
+@misc{FUMEA-F-Dense,
+  author = {Umut Aytuğ Semerci},
+  title = {FUMEA-F Dense: Dense model of Financial Frontier Unified Multi-Expert Agent},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/uaytug/fumea-f-dense}
+}
+```
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diff --git a/added_tokens.json b/added_tokens.json
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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}
diff --git a/config.json b/config.json
new file mode 100644
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--- /dev/null
+++ b/config.json
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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 131072,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.0.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936,
+  "rope_scaling": {
+    "type": "yarn",
+    "factor": 4.0,
+    "original_max_position_embeddings": 32768
+  }
+}
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diff --git a/generation_config.json b/generation_config.json
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--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,15 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.9,
+  "transformers_version": "4.51.3",
+  "repetition_penalty": 1.1,
+  "max_new_tokens": 8192
+}
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diff --git a/mergekit_config.yml b/mergekit_config.yml
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--- /dev/null
+++ b/mergekit_config.yml
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+base_model: marketeam/Qwen-Marketing
+dtype: bfloat16
+merge_method: dare_ties
+modules:
+  default:
+    slices:
+    - sources:
+      - layer_range: [0, 36]
+        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
+  normalize: 1.0
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+  ],
+  "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
+}