diff --git a/pdm.lock b/pdm.lock
index b13fe5f..83c73ec 100644
--- a/pdm.lock
+++ b/pdm.lock
@@ -5,7 +5,7 @@
 groups = ["default", "dev"]
 strategy = ["cross_platform", "inherit_metadata"]
 lock_version = "4.4.1"
-content_hash = "sha256:5266c91187a20b13682380660c9795b4ea9c2f2c2ad5370e97ab83ec920ece84"
+content_hash = "sha256:4a00d257bb6f7996524921a49a47cd158a4c00501fdd3fa89589d4d0751fb434"
 
 [[package]]
 name = "accelerate"
@@ -351,7 +351,7 @@ name = "exceptiongroup"
 version = "1.2.1"
 requires_python = ">=3.7"
 summary = "Backport of PEP 654 (exception groups)"
-groups = ["default"]
+groups = ["default", "dev"]
 marker = "python_version < \"3.11\""
 files = [
     {file = "exceptiongroup-1.2.1-py3-none-any.whl", hash = "sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad"},
@@ -615,7 +615,7 @@ name = "iniconfig"
 version = "2.0.0"
 requires_python = ">=3.7"
 summary = "brain-dead simple config-ini parsing"
-groups = ["default"]
+groups = ["dev"]
 files = [
     {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
@@ -1231,7 +1231,7 @@ name = "pluggy"
 version = "1.5.0"
 requires_python = ">=3.8"
 summary = "plugin and hook calling mechanisms for python"
-groups = ["default"]
+groups = ["dev"]
 files = [
     {file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"},
     {file = "pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1"},
@@ -1406,7 +1406,7 @@ name = "pytest"
 version = "8.2.1"
 requires_python = ">=3.8"
 summary = "pytest: simple powerful testing with Python"
-groups = ["default"]
+groups = ["dev"]
 dependencies = [
     "colorama; sys_platform == \"win32\"",
     "exceptiongroup>=1.0.0rc8; python_version < \"3.11\"",
@@ -1861,7 +1861,7 @@ name = "tomli"
 version = "2.0.1"
 requires_python = ">=3.7"
 summary = "A lil' TOML parser"
-groups = ["default", "dev"]
+groups = ["dev"]
 marker = "python_version < \"3.11\""
 files = [
     {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
diff --git a/pyproject.toml b/pyproject.toml
index 608ce12..1b49966 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,7 +18,6 @@ dependencies = [
     "numpy>=1.26.4",
     "pandas>=2.2.1",
     "pymongo>=4.6.3",
-    "pytest>=8.0.1",
     "tensorboardX>=2.6.2.2",
     "torch>=2.2.0",
     "tqdm>=4.66.2",
@@ -41,6 +40,7 @@ license = {text = "MIT"}
 dev = [
     "-e file:///${PROJECT_ROOT}/TransformerLens#egg=transformer-lens",
     "mypy>=1.10.0",
+    "pytest>=8.0.1",
 ]
 
 [tool.mypy]
diff --git a/src/lm_saes/runner.py b/src/lm_saes/runner.py
index 336ebb9..ad868a8 100644
--- a/src/lm_saes/runner.py
+++ b/src/lm_saes/runner.py
@@ -147,11 +147,23 @@ def language_model_sae_eval_runner(cfg: LanguageModelSAERunnerConfig):
     hf_model = AutoModelForCausalLM.from_pretrained(
         cfg.model_name, cache_dir=cfg.cache_dir, local_files_only=cfg.local_files_only
     )
+
+    hf_tokenizer = AutoTokenizer.from_pretrained(
+        (
+            cfg.model_name
+            if cfg.model_from_pretrained_path is None
+            else cfg.model_from_pretrained_path
+        ),
+        trust_remote_code=True,
+        use_fast=True,
+        add_bos_token=True,
+    )
     model = HookedTransformer.from_pretrained(
         cfg.model_name,
         device=cfg.device,
         cache_dir=cfg.cache_dir,
         hf_model=hf_model,
+        tokenizer=hf_tokenizer,
         dtype=cfg.dtype,
     )
     model.eval()
@@ -206,38 +218,58 @@ def sample_feature_activations_runner(cfg: LanguageModelSAEAnalysisConfig):
         cache_dir=cfg.cache_dir,
         local_files_only=cfg.local_files_only,
     )
+    hf_tokenizer = AutoTokenizer.from_pretrained(
+        (
+            cfg.model_name
+            if cfg.model_from_pretrained_path is None
+            else cfg.model_from_pretrained_path
+        ),
+        trust_remote_code=True,
+        use_fast=True,
+        add_bos_token=True,
+    )
     model = HookedTransformer.from_pretrained(
         cfg.model_name,
         device=cfg.device,
         cache_dir=cfg.cache_dir,
         hf_model=hf_model,
+        tokenizer=hf_tokenizer,
         dtype=cfg.dtype,
     )
     model.eval()
 
     client = MongoClient(cfg.mongo_uri, cfg.mongo_db)
+    client.remove_dictionary(cfg.exp_name, cfg.exp_series)
     client.create_dictionary(cfg.exp_name, cfg.d_sae, cfg.exp_series)
 
     for chunk_id in range(cfg.n_sae_chunks):
         activation_store = ActivationStore.from_config(model=model, cfg=cfg)
-        result = sample_feature_activations(sae, model, activation_store, cfg, chunk_id, cfg.n_sae_chunks)
+        result = sample_feature_activations(
+            sae, model, activation_store, cfg, chunk_id, cfg.n_sae_chunks
+        )
 
         for i in range(len(result["index"].cpu().numpy().tolist())):
-            client.update_feature(cfg.exp_name, result["index"][i].item(), {
-                "act_times": result["act_times"][i].item(),
-                "max_feature_acts": result["max_feature_acts"][i].item(),
-                "feature_acts_all": result["feature_acts_all"][i]
-                .cpu()
-                .float()
-                .numpy(),  # use .float() to convert bfloat16 to float32
-                "analysis": [
-                    {
-                        "name": v["name"],
-                        "feature_acts": v["feature_acts"][i].cpu().float().numpy(),
-                        "contexts": v["contexts"][i].cpu().numpy(),
-                    } for v in result["analysis"]
-                ]
-            }, dictionary_series=cfg.exp_series)
+            client.update_feature(
+                cfg.exp_name,
+                result["index"][i].item(),
+                {
+                    "act_times": result["act_times"][i].item(),
+                    "max_feature_acts": result["max_feature_acts"][i].item(),
+                    "feature_acts_all": result["feature_acts_all"][i]
+                    .cpu()
+                    .float()
+                    .numpy(),  # use .float() to convert bfloat16 to float32
+                    "analysis": [
+                        {
+                            "name": v["name"],
+                            "feature_acts": v["feature_acts"][i].cpu().float().numpy(),
+                            "contexts": v["contexts"][i].cpu().numpy(),
+                        }
+                        for v in result["analysis"]
+                    ],
+                },
+                dictionary_series=cfg.exp_series,
+            )
 
         del result
         del activation_store
@@ -257,11 +289,22 @@ def features_to_logits_runner(cfg: FeaturesDecoderConfig):
         cache_dir=cfg.cache_dir,
         local_files_only=cfg.local_files_only,
     )
+    hf_tokenizer = AutoTokenizer.from_pretrained(
+        (
+            cfg.model_name
+            if cfg.model_from_pretrained_path is None
+            else cfg.model_from_pretrained_path
+        ),
+        trust_remote_code=True,
+        use_fast=True,
+        add_bos_token=True,
+    )
     model = HookedTransformer.from_pretrained(
         cfg.model_name,
         device=cfg.device,
         cache_dir=cfg.cache_dir,
         hf_model=hf_model,
+        tokenizer=hf_tokenizer,
         dtype=cfg.dtype,
     )
     model.eval()
diff --git a/ui/bun.lockb b/ui/bun.lockb
index 1230fbf..6ee4bfe 100755
Binary files a/ui/bun.lockb and b/ui/bun.lockb differ