Initial release v0.1 - experimental

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,190 @@
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+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
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+      incidental, or consequential damages of any character arising as a
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+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
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README.md

@@ -1,3 +1,140 @@
----
-license: apache-2.0
----
+---
+base_model: Qwen/Qwen3-4B-Instruct-2507
+library_name: peft
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- lora
+- sft
+- trading
+- hyperliquid
+- position-sizing
+- finance
+language:
+- en
+---
+
+# HyperLLM-4b-0.1
+
+A LoRA fine-tune of [Qwen3-4B-Instruct](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) specialized for position sizing calculations, parameter validation, and API operations on the [Hyperliquid](https://hyperliquid.xyz) decentralized exchange.
+
+> **Note:** This is an experimental pre-release (0.1). Known issues include parameter validation regression. See [Limitations](#limitations).
+
+## Model Description
+
+HyperLLM-4b-0.1 is a domain-adapted assistant for cryptocurrency trading on Hyperliquid. It is designed to:
+
+- **Calculate position sizes** based on account balance, risk percentage, entry price, and stop loss
+- **Validate trading parameters** against exchange constraints (lot sizes, leverage limits, price precision)
+- **Generate correctly formatted API calls** for Hyperliquid's REST and WebSocket APIs
+- **Answer questions** about Hyperliquid mechanics, margin modes, and order types
+
+This model is a **calculation assistant**, not an autonomous trading agent. It provides recommendations that should be validated by application-layer safety checks and human oversight.
+
+## Intended Use
+
+- Position sizing calculations for risk-managed trading
+- Parameter validation before order submission
+- API call generation for Hyperliquid integration
+- Educational tool for understanding Hyperliquid mechanics
+
+## Out-of-Scope Use
+
+- Autonomous trade execution without human oversight
+- Financial advice or investment recommendations
+- Use with exchanges other than Hyperliquid (parameters are exchange-specific)
+
+## Training Results
+
+| Category | Baseline | Trained | Change |
+|----------|----------|---------|--------|
+| Overall | 36% | 64% | +28% |
+| Trading Mechanics | 20% | 80% | +60% |
+| Factual Knowledge | 60% | 80% | +20% |
+| API Structure | 17% | 50% | +33% |
+| Parameter Validation | 75% | 50% | -25% |
+
+> **Note:** Parameter validation regressed due to data imbalance in v1 training. This is addressed in subsequent versions.
+
+## Training Details
+
+- **Base Model:** Qwen/Qwen3-4B-Instruct-2507
+- **Method:** QLoRA (4-bit quantization + LoRA)
+- **LoRA Rank:** 32
+- **LoRA Alpha:** 64
+- **Training Examples:** ~2,400 synthetic examples
+- **Categories:** Position sizing, API examples, knowledge Q&A, adversarial percentages
+- **Hardware:** NVIDIA RTX 3080 (12GB VRAM)
+- **Training Time:** ~3 hours
+
+## How to Use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-4B-Instruct-2507",
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "Qwen/Qwen3-4B-Instruct-2507",
+    trust_remote_code=True
+)
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "UVLabs/HyperLLM-4b-0.1")
+
+# Example: Position sizing calculation
+messages = [
+    {"role": "system", "content": "You are a trading assistant for Hyperliquid. Calculate position sizes accurately and validate parameters against exchange constraints."},
+    {"role": "user", "content": "Account: $50,000, Risk: 2%, Entry: $45,000, Stop: $43,000. What is the correct position size for BTC?"}
+]
+
+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=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Limitations
+
+- **Exchange-specific:** Trained on Hyperliquid data only; parameters may not apply to other exchanges
+- **Parameter validation regression:** v1 showed decreased accuracy on parameter validation (addressed in later versions)
+- **Not financial advice:** Outputs should be validated before use in live trading
+- **Requires safety layer:** Should be deployed with application-level position limits and human confirmation for large trades
+
+## Risks and Recommendations
+
+1. **Always validate calculations** before executing trades
+2. **Implement hard limits** in your application layer (e.g., max 10% position size, max 10x leverage)
+3. **Require human confirmation** for trades exceeding risk thresholds
+4. **Paper trade first** before using with real capital
+
+## License
+
+This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0), consistent with the base Qwen3 model license.
+
+### Qwen3 License Notice
+
+This model is a derivative of Qwen3-4B-Instruct, which is released under Apache 2.0 by Alibaba Cloud. See the [Qwen3 model card](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) for full details.
+
+## Citation
+
+```bibtex
+@misc{hyperllm2026,
+  title={HyperLLM: Domain-Adapted LLM for Hyperliquid Trading},
+  author={HyperLLM Team},
+  year={2026},
+  url={https://huggingface.co/UVLabs/HyperLLM-4b-0.1}
+}
+```
+
+## Version History
+
+| Version | Date | Notes |
+|---------|------|-------|
+| 0.1 | Feb 2026 | Experimental release, 36%→64% overall accuracy, parameter validation regression |

adapter_config.json

@@ -0,0 +1,46 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3-4B-Instruct-2507",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "down_proj",
+    "v_proj",
+    "gate_proj",
+    "up_proj",
+    "o_proj",
+    "k_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# 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' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- 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 %}

tokenizer.json

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+size 11422650

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": false,
+  "model_max_length": 1010000,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

training_args.bin

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+size 5240