Add UserLM-2.0 model card

README.md

@@ -1,3 +1,330 @@
 ---
 license: mit
+datasets:
+  - allenai/WildChat-4.8M
+language:
+  - en
+base_model:
+  - meta-llama/Llama-3.1-8B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - userlm
+  - user-simulation
+  - simulation
+  - multi-turn
 ---
+
+# UserLM-2.0
+
+<p align="center">
+  <img src="./assets/userlm-2-teaser.png" alt="UserLM-2.0 generates natural conversations that faithfully enact task intents." width="100%">
+</p>
+
+UserLM-2.0 is an 8-billion-parameter user simulator for user-AI
+conversations. Unlike typical language models trained to play the assistant,
+UserLM-2.0 generates the **user** side of a conversation. It is conditioned on
+a task intent that describes the user's high-level objective.
+
+UserLM-2.0 can:
+
+1. Generate an initial user utterance from a task intent.
+2. Generate follow-up user utterances from the conversation so far.
+3. Emit `<|endconversation|>` when it determines that the user's objective has
+   been satisfied and the conversation should end.
+
+The model is intended to support research on realistic user simulation and the
+development and evaluation of more robust assistant models.
+
+> **Paper:** The accompanying UserLM-2.0 paper is forthcoming. An arXiv link
+> will be added when it becomes available.
+
+## Model details
+
+- **Developed by:** Omer Gul (Microsoft Research intern, Spring 2026),
+  Philippe Laban (Microsoft Research), Tanya Goyal (Microsoft Research), and
+  Jennifer Neville (Microsoft Research)
+- **Model type:** Generative causal language model, 8B parameters
+- **Language:** English
+- **License:** MIT
+- **Backbone:** [`meta-llama/Llama-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
+- **Repository:** [`microsoft/UserLM-2.0`](https://huggingface.co/microsoft/UserLM-2.0)
+
+## How to use
+
+UserLM-2.0 generates user turns. Put the task intent in the system message,
+generate a user turn, append the assistant's reply, and call the model again.
+The bundled chat template automatically opens the correct user-role generation
+header.
+
+The following example uses Hugging Face Transformers directly:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "microsoft/UserLM-2.0"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    dtype=torch.bfloat16,
+    device_map="auto",
+).eval()
+
+task_intent = (
+    "You want help planning a quick vegetarian dinner for four people. "
+    "You have 45 minutes, one guest has a peanut allergy, and you need a "
+    "shopping list and cooking instructions. You prefer pasta, but only "
+    "mention that preference if the assistant asks. Your goal is not "
+    "satisfied until you receive a menu, shopping list, and cooking steps."
+)
+messages = [{"role": "system", "content": task_intent}]
+
+
+def generate_user_turn(messages: list[dict[str, str]]) -> str:
+    inputs = tokenizer.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_tensors="pt",
+        return_dict=True,
+    ).to(model.get_input_embeddings().weight.device)
+
+    with torch.inference_mode():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=128,
+            do_sample=True,
+            temperature=1.0,
+            top_p=0.8,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+        )
+
+    generated_ids = outputs[0, inputs["input_ids"].shape[1]:]
+    return tokenizer.decode(generated_ids, skip_special_tokens=True).strip()
+
+
+# First user turn
+user_turn = generate_user_turn(messages)
+messages.append({"role": "user", "content": user_turn})
+print("User:", user_turn)
+
+# Supply an assistant response, then generate the next user turn.
+messages.append(
+    {
+        "role": "assistant",
+        "content": "I can help with that. Would you prefer pasta, tacos, or a rice bowl?",
+    }
+)
+user_turn = generate_user_turn(messages)
+
+if "<|endconversation|>" not in user_turn:
+    messages.append({"role": "user", "content": user_turn})
+    print("User:", user_turn)
+else:
+    print("The simulated user ended the conversation.")
+```
+
+### Example output
+
+The following user turns were produced by UserLM-2.0 with
+`temperature=1.0` and `top_p=0.8`. Assistant turns were supplied by the
+simulation harness.
+
+> **UserLM-2.0:** hi. i need help with a dinner. i have 45 minutes. i need to
+> make a dinner for 4 people. 1 of the people has a peanut allergy. i need a
+> menu, shopping list, and step by step cooking instructions. if you need any
+> more information from me, let me know.
+>
+> **Assistant:** I can help with that. Would you prefer pasta, tacos, or a rice
+> bowl?
+>
+> **UserLM-2.0:** pasta.
+>
+> **Assistant:** Let's make chickpea tomato pasta with a green salad. For four
+> people, buy 12 oz pasta, two cans of chickpeas, one 28 oz can of crushed
+> tomatoes, garlic, one onion, olive oil, Italian herbs, one lemon, mixed
+> greens, one cucumber, and Parmesan or a vegetarian alternative. The menu is
+> peanut-free, but check package labels for cross-contamination. Would you like
+> the step-by-step cooking order?
+>
+> **UserLM-2.0:** yes please.
+>
+> **Assistant:** Boil the pasta while you prepare a chickpea tomato sauce. Toss
+> the greens and cucumber with lemon and olive oil, combine the pasta and
+> sauce, and serve with the salad and optional cheese. This should take about
+> 35 minutes.
+>
+> **UserLM-2.0:** `<|endconversation|>`
+
+Treat `<|endconversation|>` as a control signal: stop the rollout rather than
+showing it as dialogue in an end-user interface. Generated text is stochastic,
+so outputs will vary across runs.
+
+## Uses
+
+### Direct intended uses
+
+UserLM-2.0 is released for researchers evaluating assistant language models.
+It can simulate multi-turn conversations to help estimate how an assistant may
+perform with real users. Our evaluation provides evidence that UserLM-2.0
+produces more realistic and task-faithful user behavior than prior user
+simulators and prompted assistant models.
+
+### Downstream uses
+
+Potential research applications that were not implemented in the accompanying
+work include:
+
+1. User modeling, such as predicting user responses to a set of questions.
+2. A foundation for fine-tuning judge models.
+3. Synthetic conversation generation in conjunction with an assistant model.
+
+### Out-of-scope uses
+
+UserLM-2.0 is **not an assistant model**. It is unlikely to be useful to an end
+user who needs direct help completing a task; an assistant language model is
+more appropriate for that purpose.
+
+We do not recommend deploying UserLM-2.0 in commercial or real-world
+applications without additional testing, adaptation, and safeguards. This
+checkpoint is released for research purposes.
+
+## Risks and limitations
+
+The main objective of a user simulator is twofold:
+
+1. Faithfully enact the supplied task intent without critical omissions or
+   contradictions.
+2. Produce natural utterances that could plausibly have been written by a real
+   user.
+
+Although UserLM-2.0 improves this joint objective relative to prior work, it
+remains imperfect. Simulated conversations may omit or contradict parts of the
+task intent, introduce unsupported facts or requirements, behave unnaturally,
+or terminate at an inappropriate point.
+
+Additional limitations include:
+
+- The model was designed and evaluated in English. Performance in other
+  languages may vary and should be assessed by domain experts and native
+  speakers.
+- The model inherits biases, errors, and omissions from its
+  Llama-3.1-8B-Instruct backbone.
+- The model may inherit or amplify biases, errors, and omissions in its
+  training data, including AI-generated interpretations of conversations.
+- The model may hallucinate constraints not present in the task intent.
+  Providing a specific intent can reduce, but not eliminate, this behavior.
+- The model has not been systematically hardened against security
+  vulnerabilities such as indirect prompt injection. Systems using the model
+  should apply appropriate isolation, validation, and security controls.
+
+## Recommendations
+
+UserLM-2.0 is a research release and may require adaptation for new tasks and
+environments. The original UserLM-8B work describes four useful generation
+guardrails:
+
+1. Filtering undesirable first tokens.
+2. Avoiding premature dialogue termination.
+3. Applying minimum and maximum utterance-length thresholds.
+4. Filtering verbatim repetitions.
+
+We encourage users to adapt these guardrails to their use case and to evaluate
+task fidelity, naturalness, termination behavior, and safety before relying on
+generated simulations.
+
+## Training details
+
+### Training data
+
+UserLM-2.0 was trained on a filtered version of
+[`allenai/WildChat-4.8M`](https://huggingface.co/datasets/allenai/WildChat-4.8M).
+The filtering and processing procedure is described in the forthcoming paper.
+The processed training data and processing scripts are not included with this
+model release.
+
+### Training procedure
+
+UserLM-2.0 uses Llama-3.1-8B-Instruct as its backbone. A dedicated
+`<|endconversation|>` token was added, and the chat template was modified so
+generation begins under the user role rather than the assistant role.
+
+Training consisted of:
+
+1. **Supervised fine-tuning (SFT):** full-parameter fine-tuning on individual
+   user turns conditioned on the preceding conversation, using a batch size of
+   1,024, a learning rate of `2e-5`, and a maximum sequence length of 8,192
+   tokens.
+2. **Reinforcement learning:** post-training with GRPO to jointly optimize task
+   intent coverage, response quality, naturalness, and appropriate
+   conversation termination.
+
+Additional hyperparameters and implementation details will be available in the
+paper.
+
+## Evaluation
+
+### Testing data, factors, and metrics
+
+UserLM-2.0 was compared with competitive fine-tuned user simulators and
+prompted assistant models. Evaluation measured whether simulations faithfully
+covered the supplied task intent and whether the resulting user behavior
+appeared natural. Details of test-set construction and evaluation methodology
+will be available in the paper.
+
+### Evaluation results summary
+
+We evaluate UserLM-2.0 along three complementary dimensions:
+
+1. **Quality:** UserLM-2.0 generates coherent and contextually appropriate user
+   behavior, outperforming all evaluated fine-tuned user simulators by at least
+   12%.
+2. **Coverage:** UserLM-2.0 reliably expresses subgoals specified by the user's
+   intent, exceeding evaluated fine-tuned baselines by at least 23% and
+   matching GPT-5.6-Luna, including on challenging multi-subgoal intents.
+3. **Naturalness:** Using a general-purpose human-text classifier and
+   dataset-specific discriminators, UserLM-2.0 produces highly natural user
+   interactions. It matches specialized fine-tuned simulators while
+   substantially outperforming smaller prompted assistant models.
+
+Overall, UserLM-2.0 achieves Pareto-optimal performance across naturalness and
+task-intent fidelity. Compared with prompted assistant models, it substantially
+improves naturalness; compared with previous fine-tuned user simulators, it
+substantially improves task-intent fidelity.
+
+## Environmental impact
+
+Carbon emissions were estimated using the Machine Learning Impact calculator
+presented by Lacoste et al. (2019).
+
+- **Hardware:** NVIDIA B200
+- **Compute:** 4 GPUs for 12 hours (48 GPU-hours)
+- **Cloud provider:** Microsoft Azure
+- **Compute region:** East US
+- **Estimated carbon emissions:** 115 kg CO2e
+
+## Citation
+
+A BibTeX entry will be added when the paper is publicly available. Please do
+not cite an unpublished arXiv identifier.
+
+## Glossary
+
+**UserLM** means **User Language Model**.
+
+## Model card contact
+
+This research was conducted by members of Microsoft Research. We welcome
+feedback and collaboration. To share suggestions, ask questions, or report
+unexpected or offensive behavior, contact
+[`plaban@microsoft.com`](mailto:plaban@microsoft.com).
+
+If the team receives reports of undesired behavior or independently identifies
+issues, this repository will be updated with appropriate mitigations.
+
+## Privacy
+
+See the [Microsoft Privacy Statement](https://www.microsoft.com/en-us/privacy/privacystatement).