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Model: openalchemy/MachFund
Source: Original Platform

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+gguf/mach-fund-1-f16.gguf filter=lfs diff=lfs merge=lfs -text
+gguf/mach-fund-1-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+gguf/mach-fund-1-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: mit
+language:
+- zh
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- finance
+- chinese
+- qlora
+- private-equity
+- fund-analysis
+- distillation
+metrics:
+- loss
+---
+
+# MachFund-1
+
+A specialized Chinese private equity fund analysis model, fine-tuned from [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) using QLoRA knowledge distillation.
+
+## Overview
+
+MachFund-1 is trained to analyze Chinese private equity funds across multiple dimensions: performance analysis, risk assessment, strategy evaluation, manager background, fund comparisons, and investment advice. The model demonstrates a **68.75% improvement** over the base model on domain-specific tasks.
+
+## Training Details
+
+| Parameter | Value |
+|---|---|
+| Base Model | Qwen2.5-3B-Instruct |
+| Method | QLoRA (4-bit NF4 quantization) |
+| LoRA Rank / Alpha | 32 / 64 |
+| Training Samples | 6,976 (eval: 769) |
+| Effective Batch Size | 16 (2 x 8 grad accumulation) |
+| Learning Rate | 2e-4 (cosine schedule) |
+| Epochs | 2 |
+| Max Sequence Length | 6,144 tokens |
+| Final Training Loss | 0.9269 |
+| Training Time | 141 min on NVIDIA A100 80GB |
+| Total Steps | 872 |
+
+### Knowledge Distillation Pipeline
+
+1. **Teacher Model**: Gemini 2.5 Pro generates ~50 Q&A pairs per fund across 8 categories for 178 Chinese private equity funds
+2. **Quality Scoring**: Gemini 2.5 Flash scores each pair on 5 dimensions (accuracy, completeness, professionalism, data usage, coherence) with a threshold of 15/25
+3. **Student Training**: QLoRA fine-tuning on 6,976 high-quality filtered samples
+
+### Question Categories
+
+- Fund overview and basic information
+- Performance analysis and benchmarking
+- Risk assessment and drawdown analysis
+- Strategy analysis and market positioning
+- Manager background and track record
+- Fund comparisons (peer and category)
+- Investment advice and suitability
+- Structured data extraction
+
+## Evaluation
+
+| Gate | Metric | Result |
+|---|---|---|
+| Training Lift | Base vs Fine-tuned Score | **PASS** (4.8 to 8.1, +68.75%, threshold: 30%) |
+| Speed (FP16) | Tokens/sec on RTX 5080 | 30.1 tok/s (threshold: 50) |
+
+## Available Formats
+
+| Format | File | Size | Use Case |
+|---|---|---|---|
+| SafeTensors (FP16) | `model.safetensors` | 6.17 GB | Full precision inference |
+| GGUF Q8_0 | `gguf/mach-fund-1-Q8_0.gguf` | 3.29 GB | High-quality quantized inference |
+| GGUF Q4_K_M | `gguf/mach-fund-1-Q4_K_M.gguf` | 1.93 GB | Efficient inference, recommended |
+| GGUF F16 | `gguf/mach-fund-1-f16.gguf` | 6.18 GB | Full precision GGUF |
+
+## Usage
+
+### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("openalchemy/MachFund", torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("openalchemy/MachFund")
+
+messages = [
+    {"role": "system", "content": "You are a professional private equity fund analyst."},
+    {"role": "user", "content": "Analyze the performance of this fund"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=1024)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### llama.cpp (GGUF)
+
+```bash
+./llama-cli -m mach-fund-1-Q4_K_M.gguf -p "Analyze the risk profile of this fund" -n 512
+```
+
+### Ollama
+
+```bash
+echo 'FROM ./mach-fund-1-Q4_K_M.gguf' > Modelfile
+ollama create machfund -f Modelfile
+ollama run machfund "What is the Sharpe ratio of this fund?"
+```
+
+## Limitations
+
+- Trained specifically on Chinese private equity fund data; may not generalize to other financial domains
+- Training data reflects fund information available up to early 2026
+- Should not be used as the sole basis for investment decisions
+- Speed on consumer GPUs (RTX 5080) is below the 50 tok/s target at FP16; use GGUF Q4_K_M for faster inference
+
+## License
+
+MIT

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "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": 32768,
+  "max_window_layers": 70,
+  "model_type": "qwen2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.3.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.3.0"
+}

gguf/mach-fund-1-Q4_K_M.gguf

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gguf/mach-fund-1-Q8_0.gguf

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gguf/mach-fund-1-f16.gguf

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model.safetensors

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model_card.json

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+{
+  "model_name": "mach-fund-1",
+  "base_model": "Qwen/Qwen2.5-3B-Instruct",
+  "training": {
+    "train_samples": 6976,
+    "eval_samples": 769,
+    "epochs": 2,
+    "lora_rank": 32,
+    "batch_size": 2,
+    "grad_accum": 8,
+    "max_seq_len": 6144,
+    "total_steps": 872,
+    "train_loss": 0.9269,
+    "train_time_min": 141.0,
+    "gpu": "NVIDIA A100 80GB PCIe",
+    "completed_at": "2026-03-21 06:58:25"
+  },
+  "merged_at": "2026-03-21 16:13:47",
+  "description": "Private equity fund analysis model, distilled from Gemini 2.5 Pro"
+}

tokenizer.json

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+oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
+size 11421892

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_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|>"
+  ],
+  "is_local": true,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}