diff --git a/README.md b/README.md
index 707807d..3f86f7b 100644
--- a/README.md
+++ b/README.md
@@ -15,6 +15,39 @@
 * **Metrics:** All metric-related code can be found [here](./cik_benchmark/metrics).
 * **Experiments:** Code used to run the experiments can be found [here](./experiments).
 
+## Getting started
+
+Once the repository has been cloned locally, start by creating a conda package:
+```bash
+conda create --name CiK python=3.11
+conda activate CiK
+```
+(Note that there are compatibility issues with very recent versions of Python,
+so we recommend using Python 3.11.)
+Next, install the package:
+```bash
+pip install -e .
+```
+The `-e` option is useful for development, as it installs the package in
+editable mode. This means that changes to the code will be reflected in the
+package without needing to reinstall it. In addition, if you intend on running
+the R baselines, you will need to install additional packages. You also
+separately need to make sure that R is installed on your system, which you can
+find how to do at the [R Project](https://r-project.org) website.
+```bash
+pip install -r ./requirements-r.txt
+```
+
+You will need to set some environment variables; the full list appears in the
+next section. In particular, `NIXTLA_API_KEY` must be set for the code to
+baselines to run. Go to [](https://dashboard.nixtla.io/) to set up an account
+and get an API key.
+
+When all is done, you're ready to run baseline models. For instance, to run the
+Qwen 7B-parameter model locally (it will be downloaded automatically from Hugging Face the first time you run it), you can run:
+```bash
+python run_baselines.py --exp-spec experiments/direct-prompt-models/qwen_7b_instruct_ctx_g2.json
+```
 
 ## Setting environment variables
 
diff --git a/cik_benchmark/baselines/direct_prompt.py b/cik_benchmark/baselines/direct_prompt.py
index 9e83258..756a125 100644
--- a/cik_benchmark/baselines/direct_prompt.py
+++ b/cik_benchmark/baselines/direct_prompt.py
@@ -89,25 +89,6 @@ def huggingface_instruct_model_client(
     future_timestamps=None,
     **kwargs,
 ):
-    if constrained_decoding:
-        assert (
-            future_timestamps is not None
-        ), "Future timestamps must be provided for constrained decoding"
-
-    def constrained_decoding_regex(required_timestamps):
-        """
-        Generates a regular expression to force the model output
-        to satisfy the required format and provide values for
-        all required timestamps
-
-        """
-        timestamp_regex = "".join(
-            [
-                r"\(\s*{}\s*,\s*[-+]?\d+(\.\d+)?\)\n".format(re.escape(ts))
-                for ts in required_timestamps
-            ]
-        )
-        return r"<forecast>\n{}<\/forecast>".format(timestamp_regex)
 
     # Make generation pipeline
     pipe = pipeline(
@@ -117,11 +98,34 @@ def constrained_decoding_regex(required_timestamps):
         device_map="auto",
     )
 
-    # Build a regex parser with the generated regex
-    parser = RegexParser(constrained_decoding_regex(future_timestamps))
-    prefix_function = build_transformers_prefix_allowed_tokens_fn(
-        pipe.tokenizer, parser
-    )
+    # If constrained decoding, build a prefix function. This requires the future
+    # timestamps to be specified, so we can make them part of the regex
+    prefix_function = None
+    if constrained_decoding:
+        assert (
+            future_timestamps is not None
+        ), "Future timestamps must be provided for constrained decoding"
+
+        def constrained_decoding_regex(required_timestamps):
+            """
+            Generates a regular expression to force the model output
+            to satisfy the required format and provide values for
+            all required timestamps
+
+            """
+            timestamp_regex = "".join(
+                [
+                    r"\(\s*{}\s*,\s*[-+]?\d+(\.\d+)?\)\n".format(re.escape(ts))
+                    for ts in required_timestamps
+                ]
+            )
+            return r"<forecast>\n{}<\/forecast>".format(timestamp_regex)
+
+        # Build a regex parser with the generated regex
+        parser = RegexParser(constrained_decoding_regex(future_timestamps))
+        prefix_function = build_transformers_prefix_allowed_tokens_fn(
+            pipe.tokenizer, parser
+        )
 
     # Now extract the assistant's reply
     choices = []
diff --git a/experiments/direct-prompt-models/qwen_1.5b_instruct_ctx_g2.json b/experiments/direct-prompt-models/qwen_1.5b_instruct_ctx_g2.json
new file mode 100644
index 0000000..82aebd2
--- /dev/null
+++ b/experiments/direct-prompt-models/qwen_1.5b_instruct_ctx_g2.json
@@ -0,0 +1,4 @@
+[
+    {"label": "CC-Qwen-2.5-1.5B-Instruct (ctx)", "method": "directprompt", "llm": "qwen2.5-1.5B-Instruct", "use_context": true, "temperature": 1.0, 
+    "batch_size_on_retry":10, "batch_size":10, "n_retries": 10}
+]
diff --git a/run_baselines.py b/run_baselines.py
index 69b8502..3541d99 100644
--- a/run_baselines.py
+++ b/run_baselines.py
@@ -217,12 +217,13 @@ def experiment_directprompt(
         "gemma-2-27B-instruct": {"input": 0.0, "output": 0.0},  # Toolkit
     }
     if not llm.startswith("openrouter-") and llm not in openai_costs:
-        raise ValueError(f"Invalid model: {llm} -- Not in cost dictionary")
+        # Log a warning if the model is not in the cost dictionary
+        logging.warning(f"Model {llm} not in cost dictionary; assuming zero token cost")
 
     dp_forecaster = DirectPrompt(
         model=llm,
         use_context=use_context,
-        token_cost=openai_costs[llm] if not llm.startswith("openrouter-") else None,
+        token_cost=None if llm.startswith("openrouter-") else openai_costs.get(llm, None),
         batch_size=batch_size,
         batch_size_on_retry=batch_size_on_retry,
         n_retries=n_retries,