Upload merged Qwen3-4B-Instruct-2507 model (auto-generated README)

README.md

@@ -1,107 +1,202 @@
 ---
 base_model: unsloth/Qwen2.5-7B-Instruct
-datasets:
-- u-10bei/sft_alfworld_trajectory_dataset_v5
-- u-10bei/dbbench_sft_dataset_react_v4
-language:
-- en
-license: apache-2.0
 library_name: peft
-pipeline_tag: text-generation
-tags:
-- lora
-- agent
-- tool-use
-- alfworld
-- dbbench
 ---
 
-# ＜Qwen2.5-7B-Agent-Mixed-Trajectory-LoRA＞
+# Model Card for Model ID
 
-This repository provides a **LoRA adapter** fine-tuned from
-**unsloth/Qwen2.5-7B-Instruct** using **LoRA + Unsloth**.
+<!-- Provide a quick summary of what the model is/does. -->
 
-This repository contains **LoRA adapter weights only**.
-The base model must be loaded separately.
 
-## Training Objective
 
-This adapter is trained to improve **multi-turn agent task performance**
-on ALFWorld (household tasks) and DBBench (database operations).
+## Model Details
 
-Loss is applied to **all assistant turns** in the multi-turn trajectory,
-enabling the model to learn environment observation, action selection,
-tool use, and recovery from errors.
+### Model Description
 
-## Dataset Construction
+<!-- Provide a longer summary of what this model is. -->
 
-Training data was built by mixing and preprocessing two trajectory datasets:
 
-- **ALFWorld** (`u-10bei/sft_alfworld_trajectory_dataset_v5`): 2,327 samples after cleaning
-- **DBBench** (`u-10bei/dbbench_sft_dataset_react_v4`): 1,200 samples after cleaning
 
-Preprocessing steps applied:
-1. Structural validation (removes empty / single-turn samples)
-2. Chat template tag contamination removal (`htags` pattern)
-3. Hallucinated object ID removal — ALFWorld only (e.g. `bowl 99`)
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
 
-Category-level upsampling was applied to reinforce weak task types
-identified from evaluation results of a prior model:
+### Model Sources [optional]
 
-| Category | Multiplier | Reason |
-|---|---|---|
-| ALFWorld multi-object | ×3 | 0% success rate in prior eval |
-| ALFWorld cool | ×2 | 12% success rate |
-| ALFWorld examine | ×1.5 | 12% success rate |
-| DBBench aggregation-MAX | ×3 | 17% accuracy in prior eval |
-| DBBench INSERT | ×2 | 32% accuracy |
-| DBBench counting | ×2 | 36% accuracy |
+<!-- Provide the basic links for the model. -->
 
-Final dataset size after mixing and upsampling: **5,169 samples**
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-## Training Configuration
+## Uses
 
-| Parameter | Value |
-|---|---|
-| Base model | unsloth/Qwen2.5-7B-Instruct |
-| Method | LoRA + Unsloth (Colab Pro A100) |
-| Max sequence length | 4096 |
-| Epochs | 3 |
-| Learning rate | 8e-6 |
-| LoRA r | 64 |
-| LoRA alpha | 128 |
-| LoRA dropout | 0 |
-| LoRA target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
-| Per-device batch size | 4 |
-| Gradient accumulation | 4 (effective batch size: 16) |
-| Warmup ratio | 0.1 |
-| Weight decay | 0.05 |
-| Seed | 3407 |
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-## Usage
+### Direct Use
 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-import torch
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-base = "unsloth/Qwen2.5-7B-Instruct"
-adapter = "UtsuSl0th/your-repo-name"
+[More Information Needed]
 
-tokenizer = AutoTokenizer.from_pretrained(base)
-model = AutoModelForCausalLM.from_pretrained(
-    base,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-)
-model = PeftModel.from_pretrained(model, adapter)
-```
+### Downstream Use [optional]
 
-## Sources & Terms (IMPORTANT)
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-Training data:
-- `u-10bei/sft_alfworld_trajectory_dataset_v5`
-- `u-10bei/dbbench_sft_dataset_react_v4`
+[More Information Needed]
 
-Dataset License: MIT License. These datasets are used and distributed under the terms of the MIT License.  
-Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.13.2

adapter_config.json

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     "down_proj",
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+    "q_proj",
+    "o_proj",
+    "up_proj",
+    "k_proj"
   ],
   "task_type": "CAUSAL_LM",
   "use_dora": false,

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config.json

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+  "unsloth_version": "2025.12.7",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

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+    "model.layers.9.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.self_attn.k_proj.bias": "model-00002-of-00004.safetensors",
+    "model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.self_attn.q_proj.bias": "model-00002-of-00004.safetensors",
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+    "model.layers.9.self_attn.v_proj.bias": "model-00002-of-00004.safetensors",
+    "model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
+    "model.norm.weight": "model-00003-of-00004.safetensors"
+  }
+}

tokenizer_config.json

@@ -201,7 +201,7 @@
   "extra_special_tokens": {},
   "model_max_length": 32768,
   "pad_token": "<|vision_pad|>",
-  "padding_side": "right",
+  "padding_side": "left",
   "split_special_tokens": false,
   "tokenizer_class": "Qwen2Tokenizer",
   "unk_token": null